Automated pipeline for de novo metabolite identification using mass-spectrometry-based metabolomics.

Metabolite identification is one of the biggest bottlenecks in metabolomics. Identifying human metabolites poses experimental, analytical, and computational challenges. Here we present a pipeline of previously developed cheminformatic tools and demonstrate how it facilitates metabolite identification using solely LC/MS(n) data. These tools process, annotate, and compare MS(n) data, and propose candidate structures for unknown metabolites either by identity assignment of identical mass spectral trees or by de novo identification using substructures of similar trees. The working and performance of this metabolite identification pipeline is demonstrated by applying it to LC/MS(n) data of urine samples. From human urine, 30 MS(n) trees of unknown metabolites were acquired, processed, and compared to a reference database containing MS(n) data of known metabolites. From these 30 unknowns, we could assign a putative identity for 10 unknowns by finding identical fragmentation trees. For 11 unknowns no similar fragmentation trees were found in the reference database. On the basis of elemental composition only, a large number of candidate structures/identities were possible, so these unknowns remained unidentified. The other 9 unknowns were also not found in the database, but metabolites with similar fragmentation trees were retrieved. Computer assisted structure elucidation was performed for these 9 unknowns: for 4 of them we could perform de novo identification and propose a limited number of candidate structures, and for the other 5 the structure generation process could not be constrained far enough to yield a small list of candidates. The novelty of this work is that it allows de novo identification of metabolites that are not present in a database by using MS(n) data and computational tools. We expect this pipeline to be the basis for the computer-assisted identification of new metabolites in future metabolomics studies, and foresee that further additions will allow the identification of even a larger fraction of the unknown metabolites.

Substitution patterns in methylated potato starch as revealed from the structure and composition of fragments in enzymatic digests.

The effect of the granule structure on the methylation of starch was investigated by comparing the substitution patterns of potato starch methylated in granular suspension and in solution to DS 0.3. Substitution patterns were analyzed by successive digestion with alpha-amylase and amyloglucosidase, fractionation of the resulting malto-oligosaccharide mixture by GPC on a preparative scale, and characterization of the fractions by GLC and MALDI-MS. The mass composition of fractions with intermediate and higher degree of polymerization was indicative of enhanced clustering of substituents in granular methyl starch. On the contrary, the composition of the smaller saccharides was governed by enzyme specificity, which was also reflected in strong deviations in their monomer composition. A sequencing study on selected 'pure' small saccharides confirmed and complemented previous conclusions on enzyme specificity.

Uniform procedure of (1)H NMR analysis of rat urine and toxicometabonomics Part II: comparison of NMR profiles for classification of hepatotoxicity.

A procedure of nuclear magnetic resonance (NMR) urinalysis using pattern recognition is proposed for early detection of toxicity of investigational compounds in rats. The method is applied to detect toxicity upon administration of 13 toxic reference compounds and one nontoxic control compound (mianserine) in rats. The toxic compounds are expected to induce necrosis (bromobenzene, paracetamol, carbon tetrachloride, iproniazid, isoniazid, thioacetamide), cholestasis (alpha-naphthylisothiocyanate (ANIT), chlorpromazine, ethinylestradiol, methyltestosterone, ibuprofen), or steatosis (phenobarbital, tetracycline). Animals were treated daily for 2 or 4 days except for paracetamol and bromobenzene (1 and 2 days) and carbon tetrachloride (1 day only). Urine was collected 24 h after the first and second treatment. The animals were sacrificed 24 h after the last treatment, and NMR data were compared with liver histopathology as well as blood and urine biochemistry. Pathology and biochemistry showed marked toxicity in the liver at high doses of bromobenzene, paracetamol, carbon tetrachloride, ANIT, and ibuprofen. Thioacetamide and chlorpromazine showed less extensive changes, while the influences of iproniazid, isoniazid, phenobarbital, ethinylestradiol, and tetracycline on the toxic parameters were marginal or for methyltestosterone and mianserine negligible. NMR spectroscopy revealed significant changes upon dosing in 88 NMR biomarker signals preselected with the Procrustus Rotation method on principal component discriminant analysis (PCDA) plots. Further evaluation of the specific changes led to the identification of biomarker patterns for the specific types of liver toxicity. Comparison of our rat NMR PCDA data with histopathological changes reported in humans and/or rats suggests that rat NMR urinalysis can be used to predict hepatotoxicity.

Sensitivity of (1)H NMR analysis of rat urine in relation to toxicometabonomics. Part I: dose-dependent toxic effects of bromobenzene and paracetamol.

(1)H nuclear magnetic resonance (NMR) spectroscopy of rat urine in combination with pattern recognition analysis was evaluated for early noninvasive detection of toxicity of investigational chemical entities. Bromobenzene (B) and paracetamol (P) were administered at five single oral dosages between 2 and 500 mg/kg and between 6 and 1800 mg/kg, respectively. The sensitivity of the proposed method to detect changes in the NMR spectra 24 and 48 h after single dosing was compared with histopathology and biochemical parameters in plasma and urine. Both B and P applied at the highest dosages induced liver necrosis and markedly increased aspartate aminotransferase (AST) and alanine aminotransferase (ALT) plasma levels. At dosages of 125 mg/kg B and 450 mg/kg P, liver necrosis and changes in AST and ALT were less pronounced, while at lower dose levels these effects could not be detected. Changes in kidney pathology or standard urine biochemistry were not observed at any of these dosages. Evaluation of the total NMR dataset showed 80 signals to be sensitive for B and P dosing. Principal component analysis on the reduced dataset revealed that NMR spectra were significantly different at dosages above 8 mg/kg (B) and 110 mg/kg (P) at both sampling times. This implies a 4- to 16-fold increased sensitivity of NMR versus histopathology and clinical chemistry in recognizing early events of liver toxicity.

Novel system for classifying chromatographic applications, exemplified by comprehensive two-dimensional gas chromatography and multivariate analysis.

For practical chromatographers it is extremely difficult to judge the merits and limitations of new technological developments. On the other hand, it is nearly impossible for those at the forefront of technology to judge the implications of their efforts for all specific applications of chromatography. Both chromatographers and researchers can be aided by a classification of the numerous specific applications into a few well-defined categories. In this paper, we propose such a classification of all chemical analysis by chromatography into three generic types of applications, viz. target-compound analysis, group-type separation, and fingerprinting. The requirements for each type are discussed in general terms. The classification scheme is applied to assess the benefits and limitations of comprehensive two-dimensional gas chromatography (GC x GC) and the possible additional benefits of using multivariate-analysis (MVA) techniques for each type of application. The conclusions pertaining to the generic types of applications are indicative for the implications of new developments for specific chemical analysis by chromatography.

Quantitative analysis of target components by comprehensive two-dimensional gas chromatography.

Quantitative analysis using comprehensive two-dimensional (2D) gas chromatography (GC) is still rarely reported. This is largely due to a lack of suitable software. The objective of the present study is to generate quantitative results from a large GC x GC data set, consisting of 32 chromatograms. In this data set, six target components need to be quantified. We compare the results of conventional integration with those obtained using so-called "multiway analysis methods". With regard to accuracy and precision, integration performs slightly better than Parallel Factor (PARAFAC) analysis. In terms of speed and possibilities for automation, multiway methods in general are far superior to traditional integration.

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives.

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided.

Evaluation of metabolite profiles as biomarkers for the pharmacological effects of thiazolidinediones in Type 2 diabetes mellitus patients and healthy volunteers.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: * Many studies have investigated the effects of thiazolidinediones on isolated biochemical markers (biomarkers) or sets of markers in Type 2 diabetes mellitus (T2DM) patients and healthy volunteers. * However, a limited number of parameters is not capable of capturing the broad response to pharmacological intervention with these types of (pleiotropic) drugs, which are known to activate the nuclear transcription factor peroxisome proliferator activated receptor gamma (PPARgamma). * Our study tested the new hypothesis (primary objective) that nuclear magnetic resonance (NMR)-based metabolomics, capable of providing a readout of global metabolite concentrations in biofluids, could provide a better (more holistic) picture of the the multiparametric response to pharmacological intervention with a PPARgamma agonist and thus yield a broad array of biomarkers ('fingerprint') that could be used to support and expedite clinical development of novel thiazolidinediones. WHAT THIS STUDY ADDS: * NMR-based metabolomics coupled with sophisticated bioinformatics is indeed capable of identifying rapid changes in global metabolite profiles in urine and plasma (treatment 'fingerprints'), which may be linked to the well-documented early changes in hepatic insulin senstitivity following thiazolidinedione intervention in T2DM patients. * Consequently, this approach (upon proper validation) comprises an important new addition to the early clinical development 'proof of concept' toolbox for thiazolidinediones, and may also be applicable to other classes of drugs. AIMS: To explore the usefulness of metabolomics as a method to obtain a broad array of biomarkers for the pharmacological effects of rosiglitazone (RSG) in plasma and urine samples from patients with type 2 diabetes mellitus (T2DM) and healthy volunteers (HVs). Additionally, we explored the differences in metabolite concentrations between T2DM patients and HVs to identify a putative metabolic disease fingerprint for T2DM. METHODS: (1)H nuclear magnetic resonance (NMR) spectroscopy was used to profile blood plasma and urine samples of 16 T2DM patients and 16 HVs receiving RSG 4 mg or placebo twice daily for 6 weeks. Multivariate analyses were employed to identify treatment- and disease-related effects on global endogenous metabolite profiles. RESULTS: RSG treatment led to a rapid relative reduction in urinary hippurate and aromatic amino acids as well as an increase in plasma branched chain amino acids and alanine, glutamine and glutamate in the T2DM group. No RSG treatment effects were noted in the HV group. Exploratory baseline analyses showed that urine and plasma metabolites discriminated between genders and disease state. T2DM patients showed a relative increase in urinary concentrations of several amino acids, citrate, phospho(enol)pyruvate and hippurate. Putative T2DM-related changes in plasma were largely attributable to increased plasma lipids. CONCLUSION: The results of this study indicate that NMR-based metabolomics of urine and blood plasma samples can yield a broad array of early responding biomarkers for the effects of RSG in T2DM patients, as well as nonglucose biomarkers that may reflect the T2DM state.