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.     

Genetically encoded sensors for metabolites.

BACKGROUND: Metabolomics, i.e., the multiparallel analysis of metabolite changes occurring in a cell or an organism, has become feasible with the development of highly efficient mass spectroscopic technologies. Functional genomics as a standard tool helped to identify the function of many of the genes that encode important transporters and metabolic enzymes over the past few years. Advanced expression systems and analysis technologies made it possible to study the biochemical properties of the corresponding proteins in great detail. We begin to understand the biological functions of the gene products by systematic analysis of mutants using systematic PTGS/RNAi, knockout and TILLING approaches. However, one crucial set of data especially relevant in the case of multicellular organisms is lacking: the knowledge of the spatial and temporal profiles of metabolite levels at cellular and subcellular levels. METHODS: We therefore developed genetically encoded nanosensors for several metabolites to provide a basic set of tools for the determination of cytosolic and subcellular metabolite levels in real time by using fluorescence microscopy. RESULTS: Prototypes of these sensors were successfully used in vitro and also in vivo, i.e., to measure sugar levels in fungal and animal cells. CONCLUSIONS: One of the future goals will be to expand the set of sensors to a wider spectrum of substrates by using the natural spectrum of periplasmic binding proteins from bacteria and by computational design of proteins with altered binding pockets in conjunction with mutagenesis. This toolbox can then be applied for four-dimensional imaging of cells and tissues to elucidate the spatial and temporal distribution of metabolites as a discovery tool in functional genomics, as a tool for high-throughput, high-content screening for drugs, to test metabolic models, and to analyze the interplay of cells in a tissue or organ.     

Gas chromatography/mass spectrometry in metabolic profiling of biological fluids

One of the objectives of metabonomics is to identify subtle changes in metabolite profiles between biological systems of different physiological or pathological states. Gas chromatography mass spectrometry (GC/MS) is a widely used analytical tool for metabolic profiling in various biofluids, such as urine and blood due to its high sensitivity, peak resolution and reproducibility. The availability of the GC/MS electron impact (EI) spectral library further facilitates the identification of diagnostic biomarkers and aids the subsequent mechanistic elucidation of the biological or pathological variations. With the advent of new comprehensive two dimensional GC (GCxGC) coupled to time-of-flight mass spectrometry (TOFMS), it is possible to detect more than 1200 compounds in a single analytical run. In this review, we discuss the applications of GC/MS in the metabolic profiling of urine and blood, and discuss its advances in methodologies and technologies.     

Metabolite profiles and the risk of developing diabetes

Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics). We investigated whether metabolite profiles could predict the development of diabetes. Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes. Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography-tandem mass spectrometry (LC-MS). Cases and controls were matched for age, body mass index and fasting glucose. Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine. A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile). The results were replicated in an independent, prospective cohort. These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.     

Profiling of human body fluids in healthy and diseased states using gas chromatography and mass spectrometry,with special reference to organic acids

This review summarizes recent advances in the application of gas chromatography and mass spectrometry to the study of human diseases. Emphasis is placed upon the organic acid profiles of the various body fluids. Methods for sample work-up prior to separation and mass spectrometric analysis are reviewed, and artifacts and pitfalls are discussed. Organic acid profiles, obtained with packed or capillary columns attached to mass spectrometers with or without computer systems, have led to the discovery of new normal metabolites, new metabolic disorders, and to new knowledge about a number of other diseases. Stable isotopes and gas chromatography—mass spectrometry are suitable for quantitative analysis of many compounds in the body fluids, and well suited for investigation of metabolic pathways.     

A metabolomics perspective of human brain tumours

During the past decade or so, a wealth of information about metabolites in various human brain tumour preparations (cultured cells, tissue specimens, tumours in vivo) has been accumulated by global profiling tools. Such holistic approaches to cellular biochemistry have been termed metabolomics. Inherent and specific metabolic profiles of major brain tumour cell types, as determined by proton nuclear magnetic resonance spectroscopy ((1)H MRS), have also been used to define metabolite phenotypes in tumours in vivo. This minireview examines the recent advances in the field of human brain tumour metabolomics research, including advances in MRS and mass spectrometry technologies, and data analysis.     

Mass spectrometry in studies of protein thiol chemistry and signaling: Opportunities and caveats

Mass spectrometry (MS) has become a powerful and widely utilized tool in the investigation of protein thiol chemistry, biochemistry, and biology. Very early biochemical studies of metabolic enzymes have brought to light the broad spectrum of reactivity profiles that distinguish cysteine thiols with functions in catalysis and protein stability from other cysteine residues in proteins. The development of MS methods for the analysis of proteins using electrospray ionization (ESI) or matrix-assisted laser desorption/ionization (MALDI) coupled with the emergence of high-resolution mass analyzers has been instrumental in advancing studies of thiol modifications, both in single proteins and within the cellular context. This article reviews MS instrumentation and methods of analysis employed in investigations of thiols and their reactivity toward a range of small biomolecules. A selected number of studies are detailed to highlight the advantages brought about by the MS technologies along with the caveats associated with these analyses.     

Microbial Community Profiles in Wastewaters from Onsite Wastewater Treatment Systems Technology

The aim of the study was to determine the potential of community-level physiological profiles (CLPPs) methodology as an assay for characterization of the metabolic diversity of wastewater samples and to link the metabolic diversity patterns to efficiency of select onsite biological wastewater facilities. Metabolic fingerprints obtained from the selected samples were used to understand functional diversity implied by the carbon substrate shifts. Three different biological facilities of onsite wastewater treatment were evaluated: fixed bed reactor (technology A), trickling filter/biofilter system (technology B), and aerated filter system (the fluidized bed reactor, technology C). High similarities of the microbial community functional structures were found among the samples from the three onsite wastewater treatment plants (WWTPs), as shown by the diversity indices. Principal components analysis (PCA) showed that the diversity and CLPPs of microbial communities depended on the working efficiency of the wastewater treatment technologies. This study provided an overall picture of microbial community functional structures of investigated samples in WWTPs and discerned the linkages between microbial communities and technologies of onsite WWTPs used. The results obtained confirmed that metabolic profiles could be used to monitor treatment processes as valuable biological indicators of onsite wastewater treatment technologies efficiency. This is the first step toward understanding relations of technology types with microbial community patterns in raw and treated wastewaters.     

A GC-MS metabolic profiling study of plasma samples from mice on low- and high-fat diets

Metabolic profiling of biofluids, based on the quantitative analysis of the concentration profile of their free low molecular mass metabolites, has been playing increasing role employed as a means to gain understanding of the progression of metabolic disorders, including obesity. Chromatographic methods coupled with mass spectrometry have been established as a strategy for metabolic profiling. Among these, GC-MS, targeting mainly the primary metabolism intermediates, offers high sensitivity, good peak resolution and extensive databases. However, the derivatization step required for many involatile metabolites necessitates specific data validation, normalization and analysis protocols to ensure accurate and reproducible performance. In this study, the GC-MS metabolic profiles of plasma samples from mice maintained on 12- or 15-month long low (10 kcal%) or high (60 kcal%) fat diets were obtained. The profiles of the trimethylsilyl(TMS)-methoxime(MeOx) derivatives of the free polar metabolites were acquired through GC-(ion trap)MS, using [U-(13)C]-glucose as the internal standard. After the application of a recently developed data correction and normalization/filtering protocol for GC-MS metabolomic datasets, the profiles of 48 out of the 77 detected metabolites were used in multivariate statistical analysis. Data mining suggested a decrease in the activity of the energy metabolism with age. In addition, the metabolic profiles indicated the presence of subpopulations with different physiology within the high- and low-fat diet mice, which correlated well with the difference in body weight among the animals and current knowledge about hyperglycemic conditions.     

Metabolomics: building on a century of biochemistry to guide human health

Medical diagnosis and treatment efficacy will improve significantly when a more personalized system for health assessment is implemented. This system will require diagnostics that provide sufficiently detailed information about the metabolic status of individuals such that assay results will be able to guide food, drug and lifestyle choices to maintain or improve distinct aspects of health without compromising others. Achieving this goal will use the new science of metabolomics – comprehensive metabolic profiling of individuals linked to the biological understanding of human integrative metabolism. Candidate technologies to accomplish this goal are largely available, yet they have not been brought into practice for this purpose. Metabolomic technologies must be sufficiently rapid, accurate and affordable to be routinely accessible to both healthy and acutely ill individuals. The use of metabolomic data to predict the health trajectories of individuals will require bioinformatic tools and quantitative reference databases. These databases containing metabolite profiles from the population must be built, stored and indexed according to metabolic and health status. Building and annotating these databases with the knowledge to predict how a specific metabolic pattern from an individual can be adjusted with diet, drugs and lifestyle to improve health represents a logical application of the biochemistry knowledge that the life sciences have produced over the past 100 years.     

Metabolomics‐based systematic prediction of yeast lifespan and its application for semi‐rational screening of ageing‐related mutants

Metabolomics – the comprehensive analysis of metabolites – was recently used to classify yeast mutants with no overt phenotype using raw data as metabolic fingerprints or footprints. In this study, we demonstrate the estimation of a complicated phenotype, longevity, and semi‐rational screening for relevant mutants using metabolic profiles as strain‐specific fingerprints. The fingerprints used in our experiments are profiled data consisting of individually identified and quantified metabolites rather than raw spectrum data. We chose yeast replicative lifespan as a model phenotype. Several yeast mutants that affect lifespan were selected for analysis, and they were subjected to metabolic profiling using mass spectrometry. Fingerprinting based on the profiles revealed a correlation between lifespan and metabolic profile. Amino acids and nucleotide derivatives were the main contributors to this correlation. Furthermore, we established a multivariate model to predict lifespan from a metabolic profile. The model facilitated the identification of putative longevity mutants. This work represents a novel approach to evaluate and screen complicated and quantitative phenotype by means of metabolomics.     

Metabolomics – an overview. From basic principles to potential biomarkers (part 2)

The metabolome, which represents the complete set of molecules (metabolites) of a biological sample (cell, tissue, organ, organism), is the final downstream product of the metabolic cell process that involves the genome and exogenous sources. The metabolome is characterized by a large number of small molecules with a huge diversity of chemical structures and abundances. Exploring the metabolome requires complementary analytical platforms to reach its extensive coverage. The metabolome is continually evolving, reflecting the continuous flux of metabolic and signaling pathways. Metabolomic research aims to study the biochemical processes by detecting and quantifying metabolites to obtain a metabolic picture able to give a functional readout of the physiological state. Recent advances in mass spectrometry (one of the mostly used technologies for metabolomics studies) have given the opportunity to determine the spatial distribution of metabolites in tissues. In a two-part article, we describe the usual metabolomics technologies, workflows and strategies leading to the implementation of new clinical biomarkers. In this second part, we first develop the steps of a metabolomic analysis from sample collection to biomarker validation. Then with two examples, autism spectrum disorders and Alzheimer's disease, we illustrate the contributions of metabolomics to clinical practice. Finally, we discuss the complementarity of in vivo (positron emission tomography) and in vitro (metabolomics) molecular explorations for biomarker research.     

A novel approach for nontargeted data analysis for metabolomics. Large-scale profiling of tomato fruit volatiles

To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of >20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between- and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.     

Revising the distributive networks models of West, Brown and Enquist (1997) and Banavar, Maritan and Rinaldo (1999): metabolic inequity of living tissues provides clues for the observed allometric scaling rules

Basic assumptions of two distributive network models designed to explain the 3/4 power scaling between metabolic rate and body mass are re-analysed. It is shown that these models could have consistently accounted for the observed scaling patterns if and only if body mass M had scaled as L4, where L is body length, in the model of Banavar et al. (1999, Nature 399, 130-132), or if spatial volume VF occupied by the distributive network had scaled as M3/4 in the model of West et al. (1997, Science 276, 122-126). Lack of agreement between these predictions and observational evidence invalidates both models rendering them mathematically controversial. It is further shown that consideration of distributive networks can nevertheless yield realistic values of scaling exponents under the major assumption that living organisms are designed so as to keep the mass-specific metabolic rate of important functional tissues in the vicinity of a size-independent optimum value. Mass-specific metabolic rate of subsidiary mechanical tissues can be small and vary with body mass. Different patterns of spatial distribution of metabolically active biomass within the organism result in different patterns of allometric scaling. From the available evidence the presumable optimum value of mass-specific metabolic rate of living matter is estimated to be in the vicinity of 1-10 W kg-1.     

Investigating plant-plant interference by metabolic fingerprinting

New analytical developments in post-genomic technologies are being introduced to the field of plant ecology. FT-IR fingerprinting coupled with chemometrics via cluster analysis is proposed as a tool for correlating global metabolic changes with abiotic or biotic perturbation and/or interactions. The current study concentrates on detecting chemical responses by inter-species competition between a monocotyledon Brachypodium distachyion and a dicotyledon Arabidopsis thaliana. Growth analysis of 42 days old plants showed differences in both species under competition. Clear changes in the FT-IR metabolic fingerprints of B. distachyion in competition with A. thaliana were observed, whilst there were no apparent chemical differences in the A. thaliana plant tissues. This study demonstrates the power of this approach in detecting changes in the global metabolic profiles of plants in response to biotic interactions, and we believe FT-IR is appropriate for rapid screening (10 s per sample) prior to targeted metabolite analyses.     

Tissue-culture-responsive and autotetraploidy-responsive changes in metabolic profiles of cucumber (Cucumis sativus L.)

Somaclonal variation commonly occurs during in vitro plant regeneration and may introduce unintended changes in numerous plant characters. In order to assess the range of tissue-culture-responsive changes on the biochemical level, the metabolic profiles of diploid and tetraploid cucumber R1 plants regenerated from leaf-derived callus were determined. Gas chromatography and mass spectrometry were used for monitoring of 48 metabolites and many significant changes were found in metabolic profiles of these plants as compared to a seed-derived control. Most of the changes were common to diploids and tetraploids and were effects of tissue culture. However, tetraploids showed quantitative changes in 14 metabolites, as compared to regenerated diploids. These changes include increases in serine, glucose-6P, fructose-6P, oleic acid and shikimic acid levels. Basing on this study we conclude that the variation in metabolic profiles does not correlate directly with the range of genome changes in tetraploids.     

Single cell metabolomics

Recent discoveries suggest that cells of a clonal population often display multiple metabolic phenotypes at the same time. Motivated by the success of mass spectrometry (MS) in the investigation of population-level metabolomics, the analytical community has initiated efforts towards MS-based single cell metabolomics to investigate metabolic phenomena that are buried under the population average. Here, we review the current approaches and illustrate their advantages and disadvantages. Because of significant advances in the field, different technologies are now at the verge of generating data that are useful for exploring and investigating metabolic heterogeneity.