A large-scale metabolomics study to harness chemical diversity and explore biochemical mechanisms in ryegrass.

Perennial ryegrass (Lolium perenne) is integral to temperate pastoral agriculture, which contributes most of the milk and meat production worldwide. Chemical profiles and diversity of ryegrass offer several opportunities to harness specific traits and elucidate underlying biological mechanisms for forage improvement. We conducted a large-scale metabolomics study of perennial ryegrass comprising 715 genotypes, representing 118 populations from 21 countries. Liquid/gas chromatography-mass spectrometry based targeted and non-targeted techniques were used to analyse fructan oligosaccharides, lipids, fatty acid methyl esters, polar and semi-polar compounds. Fructan diversity across all genotypes was evaluated, high- and low-sugar groups identified, and fructan accumulation mechanisms explored. Metabolites differentiating the two groups were characterised, modules and pathways they represent deduced, and finally, visualisation and interpretation provided in a biological context. We also demonstrate a workflow for large-scale metabolomics studies from raw data through to statistical and pathway analysis. Raw files and metadata are available at the MetaboLights database.

Low pyrrolizidine alkaloid levels in perennial ryegrass is associated with the absence of a homospermidine synthase gene.

BACKGROUND: Pyrrolizidine alkaloids (PAs) are a class of secondary metabolites that function as feeding deterrents in a range of different plant species. In perennial ryegrass (Lolium perenne L.) the only PAs that have been identified are the thesinine-rhamnoside group, which displays significant genetic variation. Homospermidine synthase (HSS) has evolved from deoxyhypusine synthase (DHS) and catalyses the first step in the PA pathway, making it a key candidate for the investigation of genes influencing observed PA trait variation. RESULTS: During PCR amplification and sequence analysis of DHS we identified two putative HSS genes in perennial ryegrass. One of the genes (LpHSS1) was absent in some perennial ryegrass plants. Thesinine-rhamnoside levels were measured using liquid chromatography coupled with mass spectrometry in a diverse association mapping population, consisting of 693 plants free of fungal endophytic symbionts. Association tests that accounted for population structure identified a significant association of absence of the LpHSS1 gene with lower levels of thesinine-rhamnoside PAs. HSS-like gene sequences were identified for other grass species of the Poaceae, including tall fescue, wheat, maize and sorghum. CONCLUSION: HSS is situated at the crucial first step in the PA pathway making it an important candidate gene for investigation of involvement in PA phenotypic variation. In this study, PA level in perennial ryegrass was strongly associated with the presence or absence of the LpHSS1 gene. A genetic marker, developed for the presence/absence of LpHSS1, may be used for marker-assisted breeding to either lower or increase PAs in breeding populations of perennial or Italian ryegrass to investigate a potential role in the deterrence of herbivore pests. The presence of HSS-like genes in several other Poaceae species suggests that PA biosynthesis may occur in plant family members beyond perennial ryegrass and tall fescue and identifies a potential route for manipulating PA levels.

Metabolic changes and associated cytokinin signals in response to nitrate assimilation in roots and shoots of Lolium perenne.

The efficiency of inorganic nitrogen (N) assimilation is a critical component of fertilizer use by plants and of forage production in Lolium perenne, an important pasture species worldwide. We present a spatiotemporal description of nitrate use efficiency in terms of metabolic responses and carbohydrate remobilization, together with components of cytokinin signal transduction following nitrate addition to N-impoverished plants. Perennial ryegrass (L. perenne cv. Grasslands Nui) plants were grown for 10 weeks in unfertilized soil and then treated with nitrate (5 mM) hydroponically. Metabolomic analysis by gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry revealed a dynamic interaction between N and carbon metabolism over a week-long time course represented by the relative abundance of amino acids, tricarboxylic acid intermediates and stored water-soluble carbohydrates (WSCs). The initial response to N addition was characterized by a rapid remobilization of carbon stores from the low-molecular weight WSC, along with an increase in N content and assimilation into free amino acids. Subsequently, the shoot became the main source of carbon through remobilization of a large pool of high-molecular weight WSC. Associated quantification of cytokinin levels and expression profiling of putative cytokinin response regulator genes by quantitative reverse transcription polymerase chain reaction support a role for cytokinin in the mediation of the response to N addition in perennial ryegrass. The presence of high levels of cis-zeatin-type cytokinins is discussed in the context of hormonal homeostasis under the stress of steady-state N deficiency.

Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics.

Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio (m/z) and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accurate m/z measurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae. Chromatographic behaviour, reflecting the physicochemical properties of metabolites, should also provide structural information. However, the variation in rt between analytical runs, and the complicating factors underlying the observed time shifts, make the use of such information for peak annotation a non-trivial task. To this end, we conducted Quantitative Structure-Retention Relationship (QSRR) modelling between the calculated molecular descriptors (MDs) and the experimental retention times (rts) of 93 authentic compounds analysed using hydrophilic interaction liquid chromatography (HILIC) coupled to high resolution MS. A predictive QSRR model based on Random Forests algorithm outperformed a Multiple Linear Regression based model, and achieved a high correlation between predicted rts and experimental rts (Pearson's correlation coefficient = 0.97), with mean and median absolute error of 0.52 min and 0.34 min (corresponding to 5.1 and 3.2 % error), respectively. We demonstrate that rt prediction with the precision achieved enables the systematic utilisation of rts for annotating unknown peaks detected in a metabolomics study. The application of the QSRR model with the strategy we outlined enhanced the peak annotation process by reducing the number of false positives resulting from database queries by matching accurate mass alone, and enriching the reference library. The predicted rts were validated using either authentic compounds or ion fragmentation patterns.

Quantification of stable isotope label in metabolites via mass spectrometry.

Isotope labelling experiments with stable or radioactive isotopes have long been an integral part of biological and medical research. Labelling experiments led to the discovery of new metabolic pathways and made it possible to calculate the fluxes responsible for a metabolic phenotype, i.e., the qualitative and quantitative composition of metabolites in a biological system. Prerequisite for efficient isotope labelling experiments is a reliable and precise method to analyze the redistribution of isotope label in a metabolic network. Here we describe the use of the CORRECTOR program, which utilizes matrix calculations to correct mass spectral data from stable isotope labelling experiments for the distorting effect of naturally occurring stable isotopes (NOIs). CORRECTOR facilitates and speeds up the routine quantification of experimentally introduced isotope label from multiple mass spectral readouts, which are generated by routine metabolite profiling when combined with stable isotope labelling experiments.

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.

Metabolic fluxes in an illuminated Arabidopsis rosette.

Photosynthesis is the basis for life, and its optimization is a key biotechnological aim given the problems of population explosion and environmental deterioration. We describe a method to resolve intracellular fluxes in intact Arabidopsis thaliana rosettes based on time-dependent labeling patterns in the metabolome. Plants photosynthesizing under limiting irradiance and ambient CO2 in a custom-built chamber were transferred into a (13)CO2-enriched environment. The isotope labeling patterns of 40 metabolites were obtained using liquid or gas chromatography coupled to mass spectrometry. Labeling kinetics revealed striking differences between metabolites. At a qualitative level, they matched expectations in terms of pathway topology and stoichiometry, but some unexpected features point to the complexity of subcellular and cellular compartmentation. To achieve quantitative insights, the data set was used for estimating fluxes in the framework of kinetic flux profiling. We benchmarked flux estimates to four classically determined flux signatures of photosynthesis and assessed the robustness of the estimates with respect to different features of the underlying metabolic model and the time-resolved data set.

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.

Low-carbon acclimation in carboxysome-less and photorespiratory mutants of the cyanobacterium Synechocystis sp. strain PCC 6803.

Using metabolic and transcriptomic phenotyping, we studied acclimation of cyanobacteria to low inorganic carbon (LC) conditions and the requirements for coordinated alteration of metabolism and gene expression. To analyse possible metabolic signals for LC sensing and compensating reactions, the carboxysome-less mutant ΔccmM and the photorespiratory mutant ΔglcD1/D2 were compared with wild-type (WT) Synechocystis. Metabolic phenotyping revealed accumulation of 2-phosphoglycolate (2PG) in ΔccmM and of glycolate in ΔglcD1/D2 in LC- but also in high inorganic carbon (HC)-grown mutant cells. The accumulation of photorespiratory metabolites provided evidence for the oxygenase activity of RubisCO at HC. The global gene expression patterns of HC-grown ΔccmM and ΔglcD1/D2 showed differential expression of many genes involved in photosynthesis, high-light stress and N assimilation. In contrast, the transcripts of LC-specific genes, such as those for inorganic carbon transporters and components of the carbon-concentrating mechanism (CCM), remained unchanged in HC cells. After a shift to LC, ΔglcD1/D2 and WT cells displayed induction of many of the LC-inducible genes, whereas ΔccmM lacked similar changes in expression. From the coincidence of the presence of 2PG in ΔccmM without CCM induction and of glycolate in ΔglcD1/D2 with CCM induction, we regard a direct role for 2PG as a metabolic signal for the induction of CCM during LC acclimation as less likely. Instead, our data suggest a potential role for glycolate as a signal molecule for enhanced expression of CCM genes.

Modulation of the major paths of carbon in photorespiratory mutants of synechocystis.

BACKGROUND: Recent studies using transcript and metabolite profiles of wild-type and gene deletion mutants revealed that photorespiratory pathways are essential for the growth of Synechocystis sp. PCC 6803 under atmospheric conditions. Pool size changes of primary metabolites, such as glycine and glycolate, indicated a link to photorespiration. METHODOLOGY/PRINCIPAL FINDINGS: The (13)C labelling kinetics of primary metabolites were analysed in photoautotrophically grown cultures of Synechocystis sp. PCC 6803 by gas chromatography-mass spectrometry (GC-MS) to demonstrate the link with photorespiration. Cells pre-acclimated to high CO(2) (5%, HC) or limited CO(2) (0.035%, LC) conditions were pulse-labelled under very high (2% w/w) (13)C-NaHCO(3) (VHC) conditions followed by treatment with ambient (12)C at HC and LC conditions, respectively. The (13)C enrichment, relative changes in pool size, and (13)C flux of selected metabolites were evaluated. We demonstrate two major paths of CO(2) assimilation via Rubisco in Synechocystis, i.e., from 3PGA via PEP to aspartate, malate and citrate or, to a lesser extent, from 3PGA via glucose-6-phosphate to sucrose. The results reveal evidence of carbon channelling from 3PGA to the PEP pool. Furthermore, (13)C labelling of glycolate was observed under conditions thought to suppress photorespiration. Using the glycolate-accumulating ΔglcD1 mutant, we demonstrate enhanced (13)C partitioning into the glycolate pool under conditions favouring photorespiration and enhanced (13)C partitioning into the glycine pool of the glycine-accumulating ΔgcvT mutant. Under LC conditions, the photorespiratory mutants ΔglcD1 and ΔgcvT showed enhanced activity of the additional carbon-fixing PEP carboxylase pathway. CONCLUSIONS/SIGNIFICANCE: With our approach of non-steady-state (13)C labelling and analysis of metabolite pool sizes with respective (13)C enrichments, we identify the use and modulation of major pathways of carbon assimilation in Synechocystis in the presence of high and low inorganic carbon supplies.

Sample amount alternatives for data adjustment in comparative cyanobacterial metabolomics.

Here we describe an integrative protocol for metabolite extraction and the measurement of three cellular constituents, chlorophyll a, total protein, and glycogen from the same small volume of cyanobacterial cultures that can be used as alternative sample amount parameters for data adjustment in comparative metabolome studies. We conducted recovery experiments to assess the robustness and reproducibility of the measurements obtained for the cellular constituents. Also, we have chosen three profile-intrinsic parameters derived from gas chromatography-mass spectrometry (GC/MS) data in order to test their utility for spectral data adjustment. To demonstrate the relevance of these six parameters, we analyzed three cyanobacteria with greatly different morphologies, comprising a unicellular, a filamentous, and a filamentous biofilm-forming strain. Comparative analysis of GC/MS data from cultures grown under standardized conditions indicated that adjustment of the corresponding metabolite profiles by any of the measured cellular constituents or chosen intrinsic parameters led to similar results with respect to sample cohesion and strain separation. Twenty-one metabolites significantly enriched for the carbohydrate and amine superclasses are mainly responsible for strain separation, with a majority of the remaining metabolites contributing to sample group cohesion. Therefore, we conclude that any of the parameters tested in this study can be used for spectral data adjustment of cyanobacterial strains grown under controlled conditions. However, their use for the differentiation between different stresses or physiological states within a strain remains to be shown. Interestingly, both the adjustment approaches and statistical tests applied effected the detection of metabolic differences and their patterns among the analyzed strains.

Assessment of sampling strategies for gas chromatography-mass spectrometry (GC-MS) based metabolomics of cyanobacteria.

Metabolomics is the comprehensive analysis of the small molecules that compose an organism's metabolism. The main limiting step in microbial metabolomics is the requirement for fast and efficient separation of microbes from the culture medium under conditions in which metabolism is rapidly halted. In this article we compare three different sampling strategies, quenching, filtering, and centrifugation, for arresting the metabolic activities of two morphologically diverse cyanobacteria, the unicellular Synechocystis sp. PCC 6803 and the filamentous Nostoc sp. PCC 7120 for GC-MS analysis. We demonstrate that each sampling technique produces internally consistent and reproducible data, however, cold methanol-water quenching caused leakage and substantial loss of metabolites from various compound classes, while fast filtering and centrifugation produced quite similar metabolite pool sizes, even for metabolites with predicted high turnover. This indicates that cyanobacterial metabolic pools, as measured by GC-MS, do not show high turnover under standard growing conditions. As well, using stable (13)C labeling we show the biological origin of some of the consistently observed unknown analytes. With the development of these techniques, we establish the basis for broad scale comparative metabolite profiling of cyanobacteria.

Metabolome phenotyping of inorganic carbon limitation in cells of the wild type and photorespiratory mutants of the cyanobacterium Synechocystis sp. strain PCC 6803.

The amount of inorganic carbon represents one of the main environmental factors determining productivity of photoautotrophic organisms. Using the model cyanobacterium Synechocystis sp. PCC 6803, we performed a first metabolome study with cyanobacterial cells shifted from high CO(2) (5% in air) into conditions of low CO(2) (LC; ambient air with 0.035% CO(2)). Using gas chromatography-mass spectrometry, 74 metabolites were reproducibly identified under different growth conditions. Shifting wild-type cells into LC conditions resulted in a global metabolic reprogramming and involved increases of, for example, 2-oxoglutarate (2OG) and phosphoenolpyruvate, and reductions of, for example, sucrose and fructose-1,6-bisphosphate. A decrease in Calvin-Benson cycle activity and increased usage of associated carbon cycling routes, including photorespiratory metabolism, was indicated by synergistic accumulation of the fumarate, malate, and 2-phosphoglycolate pools and a transient increase of 3-phosphoglycerate. The unexpected accumulation of 2OG with a concomitant decrease of glutamine pointed toward reduced nitrogen availability when cells are confronted with LC. Despite the increase in 2OG and low amino acid pools, we found a complete dephosphorylation of the PII regulatory protein at LC characteristic for nitrogen-replete conditions. Moreover, mutants with defined blocks in the photorespiratory metabolism leading to the accumulation of glycolate and glycine, respectively, exhibited features of LC-treated wild-type cells such as the changed 2OG to glutamine ratio and PII phosphorylation state already under high CO(2) conditions. Thus, metabolome profiling demonstrated that acclimation to LC involves coordinated changes of carbon and interacting nitrogen metabolism. We hypothesize that Synechocystis has a temporal lag of acclimating carbon versus nitrogen metabolism with carbon leading.

GC-EI-TOF-MS analysis of in vivo carbon-partitioning into soluble metabolite pools of higher plants by monitoring isotope dilution after 13CO2 labelling.

The established GC-EI-TOF-MS method for the profiling of soluble polar metabolites from plant tissue was employed for the kinetic metabolic phenotyping of higher plants. Approximately 100 typical GC-EI-MS mass fragments of trimethylsilylated and methoxyaminated metabolite derivatives were structurally interpreted for mass isotopomer analysis, thus enabling the kinetic study of identified metabolites as well as the so-called functional group monitoring of yet non-identified metabolites. The monitoring of isotope dilution after (13)CO(2) labelling was optimized using Arabidopsis thaliana Col-0 or Oryza sativa IR57111 plants, which were maximally labelled with (13)C. Carbon isotope dilution was evaluated for short (2h) and long-term (3 days) kinetic measurements of metabolite pools in root and shoots. Both approaches were shown to enable the characterization of metabolite specific partitioning processes and kinetics. Simplifying data reduction schemes comprising calculation of (13)C-enrichment from mass isotopomer distributions and of initial (13)C-dilution rates were employed. Metabolites exhibited a highly diverse range of metabolite and organ specific half-life of (13)C-label in their respective pools ((13)C-half-life). This observation implied the setting of metabolite specific periods for optimal kinetic monitoring. A current experimental design for the kinetic metabolic phenotyping of higher plants is proposed.