Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks

Now that complete genome sequences are available for a variety of organisms, the elucidation of gene functions involved in metabolism necessarily includes a better understanding of cellular responses upon mutations on all levels of gene products, mRNA, proteins, and metabolites. Such progress is essential since the observable properties of organisms - the phenotypes - are produced by the genotype in juxtaposition with the environment. Whereas much has been done to make mRNA and protein profiling possible, considerably less effort has been put into profiling the end products of gene expression, metabolites. To date, analytical approaches have been aimed primarily at the accurate quantification of a number of pre-defined target metabolites, or at producing fingerprints of metabolic changes without individually determining metabolite identities. Neither of these approaches allows the formation of an in-depth understanding of the biochemical behaviour within metabolic networks. Yet, by carefully choosing protocols for sample preparation and analytical techniques, a number of chemically different classes of compounds can be quantified simultaneously to enable such understanding. In this review, the terms describing various metabolite-oriented approaches are given, and the differences among these approaches are outlined. Metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting are considered. For each approach, a number of examples are given, and potential applications are discussed.     

Metabolite profiling of blood plasma of patients with prostate cancer

Prostate cancer is one of the most common types of cancer in men. It is though extremely important to search for specific markers including metabolites, which concentration in blood could be a diagnostic measure. In this regard, the metabolite profiling of blood plasma was performed with two groups of people: healthy volunteers (n = 30) and patients with prostate cancer, second stage (n = 40). The profiling protocol included proteins removal from blood plasma with methanol and direct analysis of metabolite fractions by mass spectrometry. Identification of the most abundant metabolites in samples was performed using an accurate mass tag and an isotope pattern methods. Cancer-specific metabolites were revealed by statistical analysis of metabolite intensities in the mass spectra. Six different metabolites were found to be cancer-specific. Two metabolites, acylcarnitine and arachidonoyl amine, have the AUC 0.97 and 0.86, respectively, which are higher than those from PSA test, 0.59.     

Gas chromatography mass spectrometry-based metabolite profiling in plants

The concept of metabolite profiling has been around for decades, but technical innovations are now enabling it to be carried out on a large scale with respect to the number of both metabolites measured and experiments carried out. Here we provide a detailed protocol for gas chromatography mass spectrometry (GC-MS)-based metabolite profiling that offers a good balance of sensitivity and reliability, being considerably more sensitive than NMR and more robust than liquid chromatography-linked mass spectrometry. We summarize all steps from collecting plant material and sample handling to derivatization procedures, instrumentation settings and evaluating the resultant chromatograms. We also define the contribution of GC-MS-based metabolite profiling to the fields of diagnostics, gene annotation and systems biology. Using the protocol described here facilitates routine determination of the relative levels of 300-500 analytes of polar and nonpolar extracts in approximately 400 experimental samples per week per machine.     

Identification of human cell responses to benzene and benzene metabolites

Benzene is a common air pollutant and confirmed carcinogen, especially in reference to the hematopoietic system. In the present study we analyzed cytokine/chemokine production by, and gene expression induction in, human peripheral blood mononuclear cells upon their exposure to the benzene metabolites catechol, hydroquinone, 1,2,4-benzenetriol, and p-benzoquinone. Protein profiling showed that benzene metabolites can stimulate the production of chemokines, the proinflammatory cytokines TNF-alpha and IL-6, and the Th2 cytokines IL-4 and IL-5. Activated cells showed concurrent suppression of anti-inflammatory cytokine IL-10 expression. We also identified changes in global gene expression patterns in response to benzene metabolite challenges by using high-density oligonucleotide microarrays. Treatment with 1,2,4-benzenetriol resulted in the suppression of genes related to the regulation of protein expression and a concomitant activation of genes that encode heat shock proteins and cytochrome P450 family members. Protein and gene expression profiling identified unique human cellular responses upon exposure to benzene and benzene metabolites.     

Metabolite profiling: from diagnostics to systems biology

The concept of metabolite profiling has been around for several decades, but only recent technical innovations have allowed metabolite profiling to be carried out on a large scale - with respect to both the number of metabolites measured and the number of experiments carried out. As a result, the power of metabolite profiling as a technology platform for diagnostics, and the research areas of gene-function analysis and systems biology, is now beginning to be fully realized.     

Exploring the temperature-stress metabolome of Arabidopsis

Metabolic profiling analyses were performed to determine metabolite temporal dynamics associated with the induction of acquired thermotolerance in response to heat shock and acquired freezing tolerance in response to cold shock. Low-M(r) polar metabolite analyses were performed using gas chromatography-mass spectrometry. Eighty-one identified metabolites and 416 unidentified mass spectral tags, characterized by retention time indices and specific mass fragments, were monitored. Cold shock influenced metabolism far more profoundly than heat shock. The steady-state pool sizes of 143 and 311 metabolites or mass spectral tags were altered in response to heat and cold shock, respectively. Comparison of heat- and cold-shock response patterns revealed that the majority of heat-shock responses were shared with cold-shock responses, a previously unknown relationship. Coordinate increases in the pool sizes of amino acids derived from pyruvate and oxaloacetate, polyamine precursors, and compatible solutes were observed during both heat and cold shock. In addition, many of the metabolites that showed increases in response to both heat and cold shock in this study were previously unlinked with temperature stress. This investigation provides new insight into the mechanisms of plant adaptation to thermal stress at the metabolite level, reveals relationships between heat- and cold-shock responses, and highlights the roles of known signaling molecules and protectants.     

Extraction parameters for metabolomics from cultured cells

The successful extraction of metabolites is a critical step in metabolite profiling. By optimizing metabolite extraction, the range and quantitative capacity of metabolomics studies can be improved. We considered eight separate extraction protocols for the preparation of a metabolite extract from cultured mammalian cells. Parameters considered included temperature, pH, and cell washing before extraction. The effects on metabolite recovery were studied using a liquid chromatography high-resolution mass spectrometry (LC–HRMS) platform that measures metabolites of diverse chemical classes, including amino acids, lipids, and sugar derivatives. The temperature considered during the extraction or the presence of formic acid, a commonly used additive, was shown to have minimal effects on the measured ion intensities of metabolites. However, washing of samples before metabolite extraction, whether with water or phosphate-buffered saline, exhibited dramatic effects on measured intensities of both intracellular and extracellular metabolites. Together, these findings present a systematic assessment of extraction conditions for metabolite profiling.     

LC–MS based global metabolite profiling: the necessity of high data quality

LC–MS based global metabolite profiling currently lacks detailed guidelines to demonstrate that the obtained data is of high enough analytical quality. Insufficient data quality may result in the failure to generate a hypothesis, or in the worst case, a false or skewed hypothesis. After assessing the literature, it is apparent that an analytically focused summary and critical discussion related to this subject would be beneficial for both beginners and experts engaged in this field. A particular focus will be placed on data quality, which we here define as the degree to which a set of parameters fulfills predetermined criteria, similar to the established guidelines for targeted analysis. However, several of these parameters are difficult to assess since holistic approaches measure thousands of metabolites in parallel and seldom include predefined knowledge of which metabolites will differ between sample groups. In this review, the following parameters will be discussed in detail: reproducibility, selectivity, certainty of metabolite identification and metabolite coverage. The review systematically describes the generic workflow for LC–MS based global metabolite profiling and highlights how each separate part may affect data quality. The last part of the review describes how data quality can be evaluated as well as identifies areas where additional improvement is needed. In this review, we provide our own analytical opinions in regards to evaluation and, to some extent, improvement of data quality.     

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Metabolomics is an ‘omics’ approach that aims toanalyze all metabolites in a biological sample comprehensively.The detailed metabolite profiling of thousands of plant sampleshas great potential for directly elucidating plant metabolicprocesses. However, both a comprehensive analysis and a highthroughput are difficult to achieve at the same time due tothe wide diversity of metabolites in plants. Here, we have establisheda novel and practical metabolomics methodology for quantifyinghundreds of targeted metabolites in a high-throughput manner.Multiple reaction monitoring (MRM) using tandem quadrupole massspectrometry (TQMS), which monitors both the specific precursorions and product ions of each metabolite, is a standard techniquein targeted metabolomics, as it enables high sensitivity, reproducibilityand a broad dynamic range. In this study, we optimized the MRMconditions for specific compounds by performing automated flowinjection analyses with TQMS. Based on a total of 61,920 spectrafor 860 authentic compounds, the MRM conditions of 497 compoundswere successfully optimized. These were applied to high-throughputautomated analysis of biological samples using TQMS coupledwith ultra performance liquid chromatography (UPLC). By thisanalysis, approximately 100 metabolites were quantified in eachof 14 plant accessions from Brassicaceae, Gramineae and Fabaceae.A hierarchical cluster analysis based on the metabolite accumulationpatterns clearly showed differences among the plant families,and family-specific metabolites could be predicted using a batch-learningself-organizing map analysis. Thus, the automated widely targetedmetabolomics approach established here should pave the way forlarge-scale metabolite profiling and comparative metabolomics.     

GC/MS-based metabolomic studies reveal key roles of glycine in regulating silk synthesis in silkworm,Bombyx mori

Metabolic profiling of silkworm, especially the factors that affect silk synthesis at the metabolic level, is little known. Herein, metabolomic method based on gas chromatography-mass spectrometry was applied to identify key metabolic changes in silk synthesis deficient silkworms. Forty-six differential metabolites were identified in Nd group with the defect of silk synthesis. Significant changes in the levels of glycine and uric acid (up-regulation), carbohydrates and free fatty acids (down-regulation) were observed. The further metabolomics of silk synthesis deficient silkworms by decreasing silk proteins synthesis using knocking out fibroin heavy chain gene or extirpating silk glands operation showed that the changes of the metabolites were almost consistent with those of the Nd group. Furthermore, the increased silk yields by supplying more glycine or its related metabolite confirmed that glycine is a key metabolite to regulate silk synthesis. These findings provide important insights into the regulation between metabolic profiling and silk synthesis.     

Phospholipid capture combined with non-linear chromatographic correction for improved serum metabolite profiling

Serum analysis with LC/MS can yield thousands of potential metabolites. However, in metabolomics, biomarkers of interest will often be of low abundance, and ionization suppression from high abundance endogenous metabolites such as phospholipids may prevent the detection of these metabolites. Here a cerium-modified column and methyl-tert-butyl-ether (MTBE) liquid–liquid extraction were employed to remove phospholipids from serum in order to obtain a more comprehensive metabolite profile. XCMS, an in-house developed data analysis software platform, showed that the intensity of existing endogenous metabolites increased, and that new metabolites were observed. This application of phospholipid capture in combination with XCMS non-linear data processing has enormous potential in metabolite profiling, for biomarker detection and quantitation.     

Plant metabolomics reveals conserved and divergent metabolic responses to salinity

New metabolic profiling technologies provide data on a wider range of metabolites than traditional targeted approaches. Metabolomic technologies currently facilitate acquisition of multivariate metabolic data using diverse, mostly hyphenated, chromatographic detection systems, such as GC-MS or liquid chromatography coupled to mass spectrometry, Fourier-transformed infrared spectroscopy or NMR-based methods. Analysis of the resulting data can be performed through a combination of non-supervised and supervised statistical methods, such as independent component analysis and analysis of variance, respectively. These methods reduce the complex data sets to information, which is relevant for the discovery of metabolic markers or for hypothesis-driven, pathway-based analysis. Plant responses to salinity involve changes in the activity of genes and proteins, which invariably lead to changes in plant metabolism. Here, we highlight a selection of recent publications in the salt stress field, and use gas chromatography time-of-flight mass spectrometry profiles of polar fractions from the plant models, Arabidopsis thaliana, Lotus japonicus and Oryza sativa to demonstrate the power of metabolite profiling. We present evidence for conserved and divergent metabolic responses among these three species and conclude that a change in the balance between amino acids and organic acids may be a conserved metabolic response of plants to salt stress.     

Application of metabolite profiling to the identification of traits in a population of tomato introgression lines

Naturally occurring variation in wild species can be used to increase the genetic diversity of cultivated crops and improve agronomic value. Populations of introgression lines carrying wild species alleles afford an opportunity to identify traits associated with the introgressed regions, and facilitate characterization of the biochemistry and genetics underlying these phenotypes. Understanding plant metabolic pathways and the interactions between genes, phenotype, and environment is fundamental to functional genomics. Successful analysis of the complex network of plant metabolism requires analytical methods able to record information on as many metabolites as possible. Metabolite profiling is used to provide a snapshot of the metabolome in samples which differ in a known factor such as genetic background. Differences between the metabolite profiles can identify those metabolites/metabolic pathways affected by the introgression and allow genetic maps for metabolic alterations to be established. A Time-of-Flight Mass Spectrometry method is presented, with associated data reduction, used for profiling aqueous metabolites fom tomato. Analysis of ripe fruits of two tomato species, Lycopersicon esculentum and L. pennellii, showed differences in the amounts of many metabolites, including organic acids and sugars. Six introgression lines, L. pennellii introgressions within L. esculentum, were also examined and showed that Principal Component Analysis can reveal subtle differences in metabolism of the introgressed lines when compared to their parents.     

Metabolite profiling for analysis of yeast stress response during very high gravity ethanol fermentations

A laboratory strain and an industrial strain of Saccharomyces cerevisiae were grown at high substrate concentration, so-called very high gravity (VHG) fermentation. Simultaneous saccharification and fermentation (SSF) was applied in a batch process using 280 g/L maltodextrin as carbon source. It was shown that known ethanol and osmotic stress responses such as decreased growth rate, lower viability, higher energy consumption, and intracellular trehalose accumulation occur in VHG SSF for both strains when compared with standard laboratory medium (20 g/L glucose). The laboratory strain was the most affected. GC-MS metabolite profiling was applied for assessing the yeast stress response influence on cellular metabolism. It was found that metabolite profiles originating from different strains and/or fermentation conditions were unique and could be distinguished with the help of multivariate data analysis. Several differences in the metabolic responses to stressing conditions were revealed, particularly the increased energy consumption of stressed cells was also reflected in increased intracellular concentrations of pyruvate and related metabolites.     

Untargeted plasma metabolite profiling reveals the broad systemic consequences of xanthine oxidoreductase inactivation in mice

A major challenge in systems biology is integration of molecular findings for individual enzyme activities into a cohesive high-level understanding of cellular metabolism and physiology/pathophysiology. However, meaningful prediction for how a perturbed enzyme activity will globally impact metabolism in a cell, tissue or intact organisms is precluded by multiple unknowns, including in vivo enzymatic rates, subcellular distribution and pathway interactions. To address this challenge, metabolomics offers the potential to simultaneously survey changes in thousands of structurally diverse metabolites within complex biological matrices. The present study assessed the capability of untargeted plasma metabolite profiling to discover systemic changes arising from inactivation of xanthine oxidoreductase (XOR), an enzyme that catalyzes the final steps in purine degradation. Using LC-MS coupled with a multivariate statistical data analysis platform, we confidently surveyed >3,700 plasma metabolites (50-1,000 Da) for differential expression in XOR wildtype vs. mice with inactivated XOR, arising from gene deletion or pharmacological inhibition. Results confirmed the predicted derangements in purine metabolism, but also revealed unanticipated perturbations in metabolism of pyrimidines, nicotinamides, tryptophan, phospholipids, Krebs and urea cycles, and revealed kidney dysfunction biomarkers. Histochemical studies confirmed and characterized kidney failure in xor-nullizygous mice. These findings provide new insight into XOR functions and demonstrate the power of untargeted metabolite profiling for systemic discovery of direct and indirect consequences of gene mutations and drug treatments.     

Systems rebalancing of metabolism in response to sulfur deprivation, as revealed by metabolome analysis of Arabidopsis plants

Sulfur is an essential macro-element in plant and animal nutrition. Plants assimilate inorganic sulfate into two sulfur-containing amino acids, cysteine and methionine. Low supply of sulfate leads to decreased sulfur pools within plant tissues. As sulfur-related metabolites represent an integral part of plant metabolism with multiple interactions, sulfur deficiency stress induces a number of adaptive responses, which must be coordinated. To reveal the coordinating network of adaptations to sulfur deficiency, metabolite profiling of Arabidopsis has been undertaken. Gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry techniques revealed the response patterns of 6,023 peaks of nonredundant ion traces and relative concentration levels of 134 nonredundant compounds of known chemical structure. Here, we provide a catalogue of the detected metabolic changes and reconstruct the coordinating network of their mutual influences. The observed decrease in biomass, as well as in levels of proteins, chlorophylls, and total RNA, gives evidence for a general reduction of metabolic activity under conditions of depleted sulfur supply. This is achieved by a systemic adjustment of metabolism involving the major metabolic pathways. Sulfur/carbon/nitrogen are partitioned by accumulation of metabolites along the pathway O-acetylserine to serine to glycine, and are further channeled together with the nitrogen-rich compound glutamine into allantoin. Mutual influences between sulfur assimilation, nitrogen imbalance, lipid breakdown, purine metabolism, and enhanced photorespiration associated with sulfur-deficiency stress are revealed in this study. These responses may be assembled into a global scheme of metabolic regulation induced by sulfur nutritional stress, which optimizes resources for seed production.