Decision tree supported substructure prediction of metabolites from GC-MS profiles

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at . All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.     

TagFinder for the quantitative analysis of gas chromatography--mass spectrometry (GC-MS)-based metabolite profiling experiments

MOTIVATION: Typical GC-MS-based metabolite profiling experiments may comprise hundreds of chromatogram files, which each contain up to 1000 mass spectral tags (MSTs). MSTs are the characteristic patterns of approximately 25-250 fragment ions and respective isotopomers, which are generated after gas chromatography (GC) by electron impact ionization (EI) of the separated chemical molecules. These fragment ions are subsequently detected by time-of-flight (TOF) mass spectrometry (MS). MSTs of profiling experiments are typically reported as a list of ions, which are characterized by mass, chromatographic retention index (RI) or retention time (RT), and arbitrary abundance. The first two parameters allow the identification, the later the quantification of the represented chemical compounds. Many software tools have been reported for the pre-processing, the so-called curve resolution and deconvolution, of GC-(EI-TOF)-MS files. Pre-processing tools generate numerical data matrices, which contain all aligned MSTs and samples of an experiment. This process, however, is error prone mainly due to (i) the imprecise RI or RT alignment of MSTs and (ii) the high complexity of biological samples. This complexity causes co-elution of compounds and as a consequence non-selective, in other words impure MSTs. The selection and validation of optimal fragment ions for the specific and selective quantification of simultaneously eluting compounds is, therefore, mandatory. Currently validation is performed in most laboratories under human supervision. So far no software tool supports the non-targeted and user-independent quality assessment of the data matrices prior to statistical analysis. TagFinder may fill this gap. Strategy: TagFinder facilitates the analysis of all fragment ions, which are observed in GC-(EI-TOF)-MS profiling experiments. The non-targeted approach allows the discovery of novel and unexpected compounds. In addition, mass isotopomer resolution is maintained by TagFinder processing. This feature is essential for metabolic flux analyses and highly useful, but not required for metabolite profiling. Whenever possible, TagFinder gives precedence to chemical means of standardization, for example, the use of internal reference compounds for retention time calibration or quantitative standardization. In addition, external standardization is supported for both compound identification and calibration. The workflow of TagFinder comprises, (i) the import of fragment ion data, namely mass, time and arbitrary abundance (intensity), from a chromatography file interchange format or from peak lists provided by other chromatogram pre-processing software, (ii) the annotation of sample information and grouping of samples into classes, (iii) the RI calculation, (iv) the binning of observed fragment ions of equal mass from different chromatograms into RI windows, (v) the combination of these bins, so-called mass tags, into time groups of co-eluting fragment ions, (vi) the test of time groups for intensity correlated mass tags, (vii) the data matrix generation and (viii) the extraction of selective mass tags supported by compound identification. Thus, TagFinder supports both non-targeted fingerprinting analyses and metabolite targeted profiling. AVAILABILITY: Exemplary TagFinder workspaces and test data sets are made available upon request to the contact authors. TagFinder is made freely available for academic use from http://www-en.mpimp-golm.mpg.de/03-research/researchGroups/01-dept1/Root_Metabolism/smp/TagFinder/index.html.     

Metabolic profiling and systematic identification of flavonoids and isoflavonoids in roots and cell suspension cultures of Medicago truncatula using HPLC-UV-ESI-MS and GC-MS

An integrated approach utilizing HPLC-UV-ESI-MS and GC-MS was used for the large-scale and systematic identification of polyphenols in Medicago truncatula root and cell culture. Under optimized conditions, we were able to simultaneously quantify and identify 35 polyphenols including 26 isoflavones, 3 flavones, 2 flavanones, 2 aurones and a chalcone. All identifications were based upon UV spectra, mass spectral characteristics of protonated molecules, tandem mass spectral data, mass measurements obtained using a quadrupole time-of-flight mass spectrometer (QtofMS), and confirmed through the co-characterization of authentic compounds. In specific instances where the stereochemistry of sugar conjugates was uncertain, subsequent enzymatic hydrolysis of the conjugate followed by GC-MS was used to assign the sugar stereochemical configuration. Comparative metabolic profiling of Medicago truncatula root and cell cultures was then performed and revealed significant differences in the isoflavonoid composition of these two tissues.     

Toward better annotation in plant metabolomics: isolation and structure elucidation of 36 specialized metabolites from Oryza sativa (rice) by using MS/MS and NMR analyses

Metabolomics plays an important role in phytochemical genomics and crop breeding; however, metabolite annotation is a significant bottleneck in metabolomic studies. In particular, in liquid chromatography–mass spectrometry (MS)-based metabolomics, which has become a routine technology for the profiling of plant-specialized metabolites, a substantial number of metabolites detected as MS peaks are still not assigned properly to a single metabolite. Oryza sativa (rice) is one of the most important staple crops in the world. In the present study, we isolated and elucidated the structures of specialized metabolites from rice by using MS/MS and NMR. Thirty-six compounds, including five new flavonoids and eight rare flavonolignan isomers, were isolated from the rice leaves. The MS/MS spectral data of the isolated compounds, with a detailed interpretation of MS fragmentation data, will facilitate metabolite annotation of the related phytochemicals by enriching the public mass spectral data depositories, including the plant-specific MS/MS-based database, ReSpect.     

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.     

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.     

Verification of automated peptide identifications from proteomic tandem mass spectra

Shotgun proteomics yields tandem mass spectra of peptides that can be identified by database search algorithms. When only a few observed peptides suggest the presence of a protein, establishing the accuracy of the peptide identifications is necessary for accepting or rejecting the protein identification. In this protocol, we describe the properties of peptide identifications that can differentiate legitimately identified peptides from spurious ones. The chemistry of fragmentation, as embodied in the 'mobile proton' and 'pathways in competition' models, informs the process of confirming or rejecting each spectral match. Examples of ion-trap and tandem time-of-flight (TOF/TOF) mass spectra illustrate these principles of fragmentation.     

Metabolite profiling in plant biology: platforms and destinations

Optimal use of genome sequences and gene-expression resources requires powerful phenotyping platforms, including those for systematic analysis of metabolite composition. The most used technologies for metabolite profiling, including mass spectral, nuclear magnetic resonance and enzyme-based approaches, have various advantages and disadvantages, and problems can arise with reliability and the interpretation of the huge datasets produced. These techniques will be useful for answering important biological questions in the future.     

Analytical strategies in metabonomics

To perform metabonomics investigations, it is necessary to generate comprehensive metabolite profiles for complex samples such as biofluids and tissue/tissue extracts. Analytical technologies that can be used to achieve this aim are constantly evolving, and new developments are changing the way in which such profiles' metabolite profiles can be generated. Here, the utility of various analytical techniques for global metabolite profiling, such as, e.g., 1H NMR, MS, HPLC-MS, and GC-MS, are explored and compared.     

The human serum metabolome

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.     

Parallel Clustering Algorithm for Large Data Sets with Applications in Bioinformatics

Large sets of bioinformatical data provide a challenge in time consumption while solving the cluster identification problem, and that is why a parallel algorithm is so needed for identifying dense clusters in a noisy background. Our algorithm works on a graph representation of the data set to be analyzed. It identifies clusters through the identification of densely intraconnected subgraphs. We have employed a minimum spanning tree (MST) representation of the graph and solve the cluster identification problem using this representation. The computational bottleneck of our algorithm is the construction of an MST of a graph, for which a parallel algorithm is employed. Our high-level strategy for the parallel MST construction algorithm is to first partition the graph, then construct MSTs for the partitioned subgraphs and auxiliary bipartite graphs based on the subgraphs, and finally merge these MSTs to derive an MST of the original graph. The computational results indicate that when running on 150 CPUs, our algorithm can solve a cluster identification problem on a data set with 1,000,000 data points almost 100 times faster than on single CPU, indicating that this program is capable of handling very large data clustering problems in an efficient manner. We have implemented the clustering algorithm as the software CLUMP.     

A unique automation platform for measuring low level radioactivity in metabolite identification studies

Generation and interpretation of biotransformation data on drugs, i.e. identification of physiologically relevant metabolites, defining metabolic pathways and elucidation of metabolite structures, have become increasingly important to the drug development process. Profiling using (14)C or (3)H radiolabel is defined as the chromatographic separation and quantification of drug-related material in a given biological sample derived from an in vitro, preclinical in vivo or clinical study. Metabolite profiling is a very time intensive activity, particularly for preclinical in vivo or clinical studies which have defined limitations on radiation burden and exposure levels. A clear gap exists for certain studies which do not require specialized high volume automation technologies, yet these studies would still clearly benefit from automation. Use of radiolabeled compounds in preclinical and clinical ADME studies, specifically for metabolite profiling and identification are a very good example. The current lack of automation for measuring low level radioactivity in metabolite profiling requires substantial capacity, personal attention and resources from laboratory scientists. To help address these challenges and improve efficiency, we have innovated, developed and implemented a novel and flexible automation platform that integrates a robotic plate handling platform, HPLC or UPLC system, mass spectrometer and an automated fraction collector.     

Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles

The non-supervised construction of a mass spectral and retention time index data base (MS/RI library) from a set of plant metabolic profiles covering major organs of potato (Solanum tuberosum), tobacco (Nicotiana tabaccum), and Arabidopsis thaliana, was demonstrated. Typically 300-500 mass spectral components with a signal to noise ratio > or =75 were obtained from GC/EI-time-of-flight (TOF)-MS metabolite profiles of methoxyaminated and trimethylsilylated extracts. Profiles from non-sample controls contained approximately 100 mass spectral components. A MS/RI library of 6205 mass spectral components was accumulated and applied to automated identification of the model compounds galactonic acid, a primary metabolite, and 3-caffeoylquinic acid, a secondary metabolite. Neither MS nor RI alone were sufficient for unequivocal identification of unknown mass spectral components. However library searches with single bait mass spectra of the respective reference substance allowed clear identification by mass spectral match and RI window. Moreover, the hit lists of mass spectral searches were demonstrated to comprise candidate components of highly similar chemical nature. The search for the model compound galactonic acid allowed identification of gluconic and gulonic acid among the top scoring mass spectral components. Equally successful was the exemplary search for 3-caffeoylquinic acid, which led to the identification of quinic acid and of the positional isomers, 4-caffeoylquinic acid, 5-caffeoylquinic acid among other still non-identified conjugates of caffeic and quinic acid. All identifications were verified by co-analysis of reference substances. Finally we applied hierarchical clustering to a complete set of pair-wise mass spectral comparisons of unknown components and reference substances with known chemical structure. We demonstrated that the resulting clustering tree depicted the chemical nature of the reference substances and that most of the nearest neighbours represented either identical components, as judged by co-elution, or conformational isomers exhibiting differential retention behaviour. Unknown components could be classified automatically by grouping with the respective branches and sub-branches of the clustering tree.     

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.     

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis,and the application of statistical tools for data analysis and interpretation

Proteomic approaches to biological research that will prove the most useful and productive require robust, sensitive, and reproducible technologies for both the qualitative and quantitative analysis of complex protein mixtures. Here we applied the isotope-coded affinity tag (ICAT) approach to quantitative protein profiling, in this case proteins that copurified with lipid raft plasma membrane domains isolated from control and stimulated Jurkat human T cells. With the ICAT approach, cysteine residues of the two related protein isolates were covalently labeled with isotopically normal and heavy versions of the same reagent, respectively. Following proteolytic cleavage of combined labeled proteins, peptides were fractionated by multidimensional chromatography and subsequently analyzed via automated tandem mass spectrometry. Individual tandem mass spectrometry spectra were searched against a human sequence database, and a variety of recently developed, publicly available software applications were used to sort, filter, analyze, and compare the results of two repetitions of the same experiment. In particular, robust statistical modeling algorithms were used to assign measures of confidence to both peptide sequences and the proteins from which they were likely derived, identified via the database searches. We show that by applying such statistical tools to the identification of T cell lipid raft-associated proteins, we were able to estimate the accuracy of peptide and protein identifications made. These tools also allow for determination of the false positive rate as a function of user-defined data filtering parameters, thus giving the user significant control over and information about the final output of large-scale proteomic experiments. With the ability to assign probabilities to all identifications, the need for manual verification of results is substantially reduced, thus making the rapid evaluation of large proteomic datasets possible. Finally, by repeating the experiment, information relating to the general reproducibility and validity of this approach to large-scale proteomic analyses was also obtained.     

Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging

We describe a mass spectrometry method, QuantMode, which improves accuracy of isobaric tag-based quantification by alleviating the pervasive problem of precursor interference, simultaneous isolation and fragmentation of impurities, through gas-phase purification. QuantMode analysis of a yeast sample 'contaminated' with interfering human peptides showed substantially improved quantitative accuracy compared to a standard scan, with a small loss of spectral identifications. This technique enables large-scale, multiplexed quantitative proteomics using isobaric tagging.