Improved 13C metabolic flux analysis in Escherichia coli metabolism: application of a high-resolution MS (GC-EI-QTOF) for comprehensive assessment of MS/MS fragments.

Gas chromatography-tandem mass spectrometry with electron ionization (GC-EI-MS/MS) provides rich information on stable-isotope labeling for 13C-metabolic flux analysis (13C-MFA). To pave the way for the routine application of tandem MS data for metabolic flux quantification, we aimed to compile a comprehensive library of GC-EI-MS/MS fragments of tert-butyldimethylsilyl (TBDMS) derivatized proteinogenic amino acids. First, we established an analytical workflow that combines high-resolution gas chromatography-quadrupole time-of-flight mass spectrometry and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments. Application of the high-mass accuracy MS procedure resulted into the identification of 129 validated precursor-product ion pairs of 13 amino acids with 30 fragments being accepted for 13C-MFA. The practical benefit of the novel tandem MS data was demonstrated by a proof-of-concept study, which confirmed the importance of the compiled library for high-resolution 13C-MFA. ONE SENTENCE SUMMARY: An analytical workflow that combines high-resolution mass spectrometry (MS) and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments, which provide positional information and therefore offering significant advantages over traditional MS to improve 13C-metabolic flux analysis.

Endotoxin Tolerance Acquisition and Altered Hepatic Fatty Acid Profile in Aged Mice.

(1) Background: Aging is linked to an altered immune response and metabolism. Inflammatory conditions, such as sepsis, COVID-19, and steatohepatitis are more prevalent in the elderly and steatosis is linked both to severe COVID-19 and sepsis. We hypothesized that aging is linked to a loss of endotoxin tolerance, which normally protects the host from excessive inflammation, and that this is accompanied by elevated levels of hepatic lipids. (2) Methods: An in vivo lipopolysaccharide (LPS) tolerance model in young and old mice was used and the cytokine serum levels were measured by ELISA. Cytokine and toll-like receptor gene expression was determined by qPCR in the lungs and the liver; hepatic fatty acid composition was assessed by GC-MS. (3) Results: The old mice showed a distinct potential for endotoxin tolerance as suggested by the serum cytokine levels and gene expression in the lung tissue. Endotoxin tolerance was less pronounced in the livers of the aged mice. However, the fatty acid composition strongly differed in the liver tissues of the young and old mice with a distinct change in the ratio of C18 to C16 fatty acids. (4) Conclusions: Endotoxin tolerance is maintained in advanced age, but changes in the metabolic tissue homeostasis may lead to an altered immune response in old individuals.

Gain time to adapt: How sorghum acquires tolerance to salinity.

Salinity is a global environmental threat to agricultural production and food security around the world. To delineate salt-induced damage from adaption events we analysed a pair of sorghum genotypes which are contrasting in their response to salt stress with respect to physiological, cellular, metabolomic, and transcriptional responses. We find that the salt-tolerant genotype Della can delay the transfer of sodium from the root to the shoot, more swiftly deploy accumulation of proline and antioxidants in the leaves and transfer more sucrose to the root as compared to its susceptible counterpart Razinieh. Instead Razinieh shows metabolic indicators for a higher extent photorespiration under salt stress. Following sodium accumulation by a fluorescent dye in the different regions of the root, we find that Della can sequester sodium in the vacuoles of the distal elongation zone. The timing of the adaptive responses in Della leaves indicates a rapid systemic signal from the roots that is travelling faster than sodium itself. We arrive at a model where resistance and susceptibility are mainly a matter of temporal patterns in signalling.

Genome-wide association study on metabolite accumulation in a wild barley NAM population reveals natural variation in sugar metabolism.

Metabolites play a key role in plants as they are routing plant developmental processes and are involved in biotic and abiotic stress responses. Their analysis can offer important information on the underlying processes. Regarding plant breeding, metabolite concentrations can be used as biomarkers instead of or in addition to genetic markers to predict important phenotypic traits (metabolic prediction). In this study, we applied a genome-wide association study (GWAS) in a wild barley nested association mapping (NAM) population to identify metabolic quantitative trait loci (mQTL). A set of approximately 130 metabolites, measured at early and late sampling dates, was analysed. For four metabolites from the early and six metabolites from the late sampling date significant mQTL (grouped as 19 mQTL for the early and 25 mQTL for the late sampling date) were found. Interestingly, all of those metabolites could be classified as sugars. Sugars are known to be involved in signalling, plant growth and plant development. Sugar-related genes, encoding mainly sugar transporters, have been identified as candidate genes for most of the mQTL. Moreover, several of them co-localized with known flowering time genes like Ppd-H1, HvELF3, Vrn-H1, Vrn-H2 and Vrn-H3, hinting on the known role of sugars in flowering. Furthermore, numerous disease resistance-related genes were detected, pointing to the signalling function of sugars in plant resistance. An mQTL on chromosome 1H in the region of 13 Mbp to 20 Mbp stood out, that alone explained up to 65% of the phenotypic variation of a single metabolite. Analysis of family-specific effects within the diverse NAM population showed the available natural genetic variation regarding sugar metabolites due to different wild alleles. The study represents a step towards a better understanding of the genetic components of metabolite accumulation, especially sugars, thereby linking them to biological functions in barley.

Reaction mechanism of the farnesyl pyrophosphate C-methyltransferase towards the biosynthesis of pre-sodorifen pyrophosphate by Serratia plymuthica 4Rx13.

Classical terpenoid biosynthesis involves the cyclization of the linear prenyl pyrophosphate precursors geranyl-, farnesyl-, or geranylgeranyl pyrophosphate (GPP, FPP, GGPP) and their isomers, to produce a huge number of natural compounds. Recently, it was shown for the first time that the biosynthesis of the unique homo-sesquiterpene sodorifen by Serratia plymuthica 4Rx13 involves a methylated and cyclized intermediate as the substrate of the sodorifen synthase. To further support the proposed biosynthetic pathway, we now identified the cyclic prenyl pyrophosphate intermediate pre-sodorifen pyrophosphate (PSPP). Its absolute configuration (6R,7S,9S) was determined by comparison of calculated and experimental CD-spectra of its hydrolysis product and matches with those predicted by semi-empirical quantum calculations of the reaction mechanism. In silico modeling of the reaction mechanism of the FPP C-methyltransferase (FPPMT) revealed a SN2 mechanism for the methyl transfer followed by a cyclization cascade. The cyclization of FPP to PSPP is guided by a catalytic dyad of H191 and Y39 and involves an unprecedented cyclopropyl intermediate. W46, W306, F56, and L239 form the hydrophobic binding pocket and E42 and H45 complex a magnesium cation that interacts with the diphosphate moiety of FPP. Six additional amino acids turned out to be essential for product formation and the importance of these amino acids was subsequently confirmed by site-directed mutagenesis. Our results reveal the reaction mechanism involved in methyltransferase-catalyzed cyclization and demonstrate that this coupling of C-methylation and cyclization of FPP by the FPPMT represents an alternative route of terpene biosynthesis that could increase the terpenoid diversity and structural space.

Can metabolic prediction be an alternative to genomic prediction in barley?

Like other crop species, barley, the fourth most important crop worldwide, suffers from the genetic bottleneck effect, where further improvements in performance through classical breeding methods become difficult. Therefore, indirect selection methods are of great interest. Here, genomic prediction (GP) based on 33,005 SNP markers and, alternatively, metabolic prediction (MP) based on 128 metabolites with sampling at two different time points in one year, were applied to predict multi-year agronomic traits in the nested association mapping (NAM) population HEB-25. We found prediction abilities of up to 0.93 for plant height with SNP markers and of up to 0.61 for flowering time with metabolites. Interestingly, prediction abilities in GP increased after reducing the number of incorporated SNP markers. The estimated effects of GP and MP were highly concordant, indicating MP as an interesting alternative to GP, being able to reflect a stable genotype-specific metabolite profile. In MP, sampling at an early developmental stage outperformed sampling at a later stage. The results confirm the value of GP for future breeding. With MP, an interesting alternative was also applied successfully. However, based on our results, usage of MP alone cannot be recommended in barley. Nevertheless, MP can assist in unravelling physiological pathways for the expression of agronomically important traits.

Shoot tip culture: a step towards 13C metabolite flux analysis of sink leaf metabolism.

BACKGROUND: Better understanding of the physiological and metabolic status of plants can only be obtained when metabolic fluxes are accurately assessed in a growing plant. Steady state 13C-MFA has been established as a routine method for analysis of fluxes in plant primary metabolism. However, the experimental system needs to be improved for continuous carbon enrichment from labelled sugars into metabolites for longer periods until complex secondary metabolism reaches steady state. RESULTS: We developed an in vitro plant culture strategy by using peppermint as a model plant with minimizing unlabelled carbon fixation where growing shoot tip was strongly dependent on labelled glucose for their carbon necessity. We optimized the light condition and detected the satisfactory phenotypical growth under the lower light intensity. Total volatile terpenes were also highest at the same light. Analysis of label incorporation into pulegone monoterpene after continuous U-13C6 glucose feeding revealed nearly 100% 13C, even at 15 days after first leaf visibility (DALV). Label enrichment was gradually scrambled with increasing light intensity and leaf age. This study was validated by showing similar levels of label enrichment in proteinogenic amino acids. The efficiency of this method was also verified in oregano. CONCLUSIONS: Our shoot tip culture depicted a method in achieving long term, stable and a high percentage of label accumulation in secondary metabolites within a fully functional growing plant system. It recommends the potential application for the investigations of various facets of plant metabolism by steady state 13C-MFA. The system also provides a greater potential to study sink leaf metabolism. Overall, we propose a system to accurately describe complex metabolic phenotypes in a growing plant.

The Metabolic Signature of Biomass Formation in Barley.

The network analysis of genome-wide transcriptome responses, metabolic signatures and enzymes' relationship to biomass formation has been studied in a diverse panel of 12 barley accessions during vegetative and reproductive stages. The primary metabolites and enzymes involved in central metabolism that determine the accumulation of shoot biomass at the vegetative stage of barley development are primarily being linked to sucrose accumulation and sucrose synthase activity. Interestingly, the metabolic and enzyme links which are strongly associated with biomass accumulation during reproductive stages are related to starch accumulation and tricarboxylic acid (TCA) cycle intermediates citrate, malate, trans-aconitate and isocitrate. Additional significant associations were also found for UDP glucose, ATP and the amino acids isoleucine, valine, glutamate and histidine during the reproductive stage. A network analysis resulted in a combined identification of metabolite and enzyme signatures indicative for grain weight accumulation that was correlated with the activity of ADP-glucose pyrophosphorylase (AGPase), a rate-limiting enzyme involved in starch biosynthesis, and with that of alanine amino transferase involved in the synthesis of storage proteins. We propose that the mechanism related to vegetative and reproductive biomass formation vs. seed biomass formation is being linked to distinct fluxes regulating sucrose, starch, sugars and amino acids as central resources. These distinct biomarkers can be used to engineer biomass production and grain weight in barley.

A unified framework for estimating parameters of kinetic biological models.

BACKGROUND: Utilizing kinetic models of biological systems commonly require computational approaches to estimate parameters, posing a variety of challenges due to their highly non-linear and dynamic nature, which is further complicated by the issue of non-identifiability. We propose a novel parameter estimation framework by combining approaches for solving identifiability with a recently introduced filtering technique that can uniquely estimate parameters where conventional methods fail. This framework first conducts a thorough analysis to identify and classify the non-identifiable parameters and provides a guideline for solving them. If no feasible solution can be found, the framework instead initializes the filtering technique with informed prior to yield a unique solution. RESULTS: This framework has been applied to uniquely estimate parameter values for the sucrose accumulation model in sugarcane culm tissue and a gene regulatory network. In the first experiment the results show the progression of improvement in reliable and unique parameter estimation through the use of each tool to reduce and remove non-identifiability. The latter experiment illustrates the common situation where no further measurement data is available to solve the non-identifiability. These results show the successful application of the informed prior as well as the ease with which parallel data sources may be utilized without increasing the model complexity. CONCLUSION: The proposed unified framework is distinct from other approaches by providing a robust and complete solution which yields reliable and unique parameter estimation even in the face of non-identifiability.

Transcription factor sensor system for parallel quantification of metabolites on-chip.

Steadily growing demands for identification and quantification of cellular metabolites in higher throughput have brought a need for new analytical technologies. Here, we developed a synthetic biological sensor system for quantifying metabolites from biological cell samples. For this, bacterial transcription factors were exploited, which bind to or dissociate from regulatory DNA elements in response to physiological changes in the cellular metabolite concentration range. Representatively, the bacterial pyruvate dehydrogenase (PdhR), trehalose (TreR), and l-arginine (ArgR) repressor proteins were functionalized to detect pyruvate, trehalose-6-phosphate (T6P), and arginine concentration in solution. For each transcription factor the mutual binding behavior between metabolite and DNA, their working range, and othogonality were determined. High-throughput, parallel processing, and automation were achieved through integration of the metabolic sensor system on a microfluidic large-scale integration (mLSI) chip platform. To demonstrate the functionality of the integrated metabolic sensor system, we measured diurnal concentration changes of pyruvate and the plant signaling molecule T6P within cell etxracts of Arabidopsis thaliana rosettes. The transcription factor sensor system is of generic nature and extendable on the microfluidic chip.

Flux visualization using VANTED/FluxMap.

The calculation of metabolic fluxes has been shown to be a valuable asset in systems biology. Several procedures are commonly used to achieve this. Flux balance analyses or metabolic flux analyses usually result in a list of reaction rates (fluxes) provided in a spreadsheet format. This makes it difficult to quickly assess general characteristics of the solution. A fast and easy mapping of these results to a graphical map template facilitates an easy visual data inspection. Here, we describe a protocol that helps in setting up user-specific network templates, mapping flux results to it, and creating multiple exportable flux maps at one time.

Flux analysis in plant metabolic networks: increasing throughput and coverage.

Quantitative information about metabolic networks has been mainly obtained at the level of metabolite contents, transcript abundance, and enzyme activities. However, the active process of metabolism is represented by the flow of matter through the pathways. These metabolic fluxes can be predicted by Flux Balance Analysis or determined experimentally by (13)C-Metabolic Flux Analysis. These relatively complicated and time-consuming methods have recently seen significant improvements at the level of coverage and throughput. Metabolic models have developed from single cell models into whole-organism dynamic models. Advances in lab automation and data handling have significantly increased the throughput of flux measurements. This review summarizes advances to increase coverage and throughput of metabolic flux analysis in plants.

Elementary flux modes, flux balance analysis, and their application to plant metabolism.

In recent years the number of sequenced and annotated plant genomes has increased significantly, and novel approaches are required to retrieve valuable information from these data sets. The field of systems biology has accelerated the simulation and prediction of phenotypes derived from specific genotypic modifications under defined growth conditions. The biochemical potential of a cell from a specific plant tissue (e.g., seed endosperm) can be derived from its genome in the form of a mathematical model by the method of metabolic network reconstruction. This model can be further analyzed by studying its network properties, analyzing feasible pathway routes through the network, or simulating possible flux distributions of the network . Here, we describe two approaches for identification of all feasible routes through the network (elementary mode analysis) and for simulation of flux distribution in the network based on plant physiological uptake and excretion rates (flux balance analysis).

High-throughput data pipelines for metabolic flux analysis in plants.

In this chapter we illustrate the methodology for high-throughput metabolic flux analysis. Central to this is developing an end to end data pipeline, crucial for integrating the wet lab experiments and analytics, combining hardware and software automation, and standardizing data representation providing importers and exporters to support third party tools. The use of existing software at the start, data extraction from the chromatogram, and the end, MFA analysis, allows for the most flexibility in this workflow. Developing iMS2Flux provided a standard, extensible, platform independent tool to act as the "glue" between these end points. Most importantly this tool can be easily adapted to support different data formats, data verification and data correction steps allowing it to be central to managing the data necessary for high-throughput MFA. An additional tool was needed to automate the MFA software and in particular to take advantage of the course grained parallel nature of high-throughput analysis and available high performance computing facilities.In combination these methods show the development of high-throughput pipelines that allow metabolic flux analysis to join as a full member of the omics family.

Flux balance analysis as an alternative method to estimate fluxes without labeling.

The analysis of plant metabolic networks essentially contributes to the understanding of the efficiency of plant systems in terms of their biotechnological usage. Metabolic fluxes are determined by biochemical parameters such as metabolite concentrations as well as enzyme properties and activities, which in turn are the result of various regulatory events at various levels between control of transcription and posttranslational regulation of enzyme protein activity. Thus, knowledge about metabolic fluxes on a large scale provides an integrated view on the functional state of a metabolically active cell, organ, or system. In this chapter, we introduce flux balance analysis as a constraint-based method for the prediction of optimal metabolic fluxes in a given metabolic network. Furthermore, we provide a step-by-step protocol for metabolic network reconstruction and constraint-based analysis using the COBRA Toolbox.

Multiscale metabolic modeling: dynamic flux balance analysis on a whole-plant scale.

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.

An improved constraint filtering technique for inferring hidden states and parameters of a biological model.

MOTIVATION: In systems biology, kinetic models represent the biological system using a set of ordinary differential equations (ODEs). The correct values of the parameters within these ODEs are critical for a reliable study of the dynamic behaviour of such systems. Typically, it is only possible to experimentally measure a fraction of these parameter values. The rest must be indirectly determined from measurements of other quantities. In this article, we propose a novel statistical inference technique to computationally estimate these unknown parameter values. By characterizing the ODEs with non-linear state-space equations, this inference technique models the unknown parameters as hidden states, which can then be estimated from noisy measurement data. RESULTS: Here we extended the square-root unscented Kalman filter SR-UKF proposed by Merwe and Wan to include constraints with the state estimation process. We developed the constrained square-root unscented Kalman filter (CSUKF) to estimate parameters of non-linear state-space models. This probabilistic inference technique was successfully used to estimate parameters of a glycolysis model in yeast and a gene regulatory network. We showed that our method is numerically stable and can reliably estimate parameters within a biologically meaningful parameter space from noisy observations. When compared with the two common non-linear extensions of Kalman filter in addition to four widely used global optimization algorithms, CSUKF is shown to be both accurate and computationally efficient. With CSUKF, statistical analysis is straightforward, as it directly provides the uncertainty on the estimation result. AVAILABILITY AND IMPLEMENTATION: Matlab code available upon request from the author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

iMS2Flux--a high-throughput processing tool for stable isotope labeled mass spectrometric data used for metabolic flux analysis.

BACKGROUND: Metabolic flux analysis has become an established method in systems biology and functional genomics. The most common approach for determining intracellular metabolic fluxes is to utilize mass spectrometry in combination with stable isotope labeling experiments. However, before the mass spectrometric data can be used it has to be corrected for biases caused by naturally occurring stable isotopes, by the analytical technique(s) employed, or by the biological sample itself. Finally the MS data and the labeling information it contains have to be assembled into a data format usable by flux analysis software (of which several dedicated packages exist). Currently the processing of mass spectrometric data is time-consuming and error-prone requiring peak by peak cut-and-paste analysis and manual curation. In order to facilitate high-throughput metabolic flux analysis, the automation of multiple steps in the analytical workflow is necessary. RESULTS: Here we describe iMS2Flux, software developed to automate, standardize and connect the data flow between mass spectrometric measurements and flux analysis programs. This tool streamlines the transfer of data from extraction via correction tools to ¹³C-Flux software by processing MS data from stable isotope labeling experiments. It allows the correction of large and heterogeneous MS datasets for the presence of naturally occurring stable isotopes, initial biomass and several mass spectrometry effects. Before and after data correction, several checks can be performed to ensure accurate data. The corrected data may be returned in a variety of formats including those used by metabolic flux analysis software such as 13CFLUX, OpenFLUX and 13CFLUX2. CONCLUSION: iMS2Flux is a versatile, easy to use tool for the automated processing of mass spectrometric data containing isotope labeling information. It represents the core framework for a standardized workflow and data processing. Due to its flexibility it facilitates the inclusion of different experimental datasets and thus can contribute to the expansion of flux analysis applications.

Towards high throughput metabolic flux analysis in plants.

Research on plant metabolism is currently experiencing the common use of various omics methods creating valuable information on the concentrations of the cell's constituents. However, little is known about in vivo reaction rates, which can be determined by Metabolic Flux Analysis (MFA), a combination of isotope labeling experiments and computer modeling of the metabolic network. Large-scale applications of this method so far have been hampered by tedious procedures of tissue culture, analytics, modeling and simulation. By streamlining the workflow of MFA, the throughput of the method could be significantly increased. We propose strategies for these improvements on various sub-steps which will move flux analysis to the medium-throughput range and closer to established methods such as metabolite profiling. Furthermore, this may enable novel applications of MFA, for example screening plant populations for traits related to the flux phenotype.