Application of direct analysis in real time ionization–mass spectrometry (DART–MS) in chicken meat metabolomics aiming at the retrospective control of feed fraud

Metabolomic fingerprinting enabled by ambient mass spectrometry employing a direct analysis in real time (DART) ion source coupled to a medium–high resolution/accurate mass time-of-flight mass spectrometer (TOFMS) was used as a tool for differentiation between chickens fed by feed that contained 5–8 % (w/w) of chicken bone meal (a banned component) and those representing a reference group, i.e. grown otherwise under the same conditions. In the first step, the sample extraction and DART–TOFMS instrumental conditions were optimized to obtain the broadest possible representation of ionizable compounds occurring in the extracts obtained from chicken muscle and feed on which experimental animals were grown. To this end, a simultaneous (all-in-one) extraction procedure was developed employing water and cyclohexane mixture as the extraction solvents. Under these conditions both polar as well as non-polar metabolites were isolated within a single extraction step. In the next step, metabolomic fingerprints of a large set of chicken muscle and feed extracts were acquired. In the final phase, the experimental data were statistically evaluated using principal component analysis and orthogonal partial least squares discriminant analysis. In general, differentiation of chicken muscle according to diet (feed with and without the addition of chicken bone meal) was feasible. Additional experiments conducted after 6 months confirmed applicability of this approach. Correct classification was obtained based on the assessment of polar as well as non-polar extracts fingerprints. However, the analysis of non-polar extracts showed that the pattern of triacylglycerols is more prone to seasonal variability and/or type of raw materials used during feed preparation which obscures the bone meal impact to some extent.     

Mass spectrometry methods in metabolomics

The review deals with metabolomics, a new and rapidly growing area directed to the comprehensive analysis of metabolites of biological objects. Metabolites are characterized by various physical and chemical properties, traditionally studied by methods of analytical chemistry focused on certain groups of chemical substances. However, current progress in mass spectrometry has led to formation of rather unified methods, such as metabolic fingerprinting and metabolomic profiling, which allow defining thousands of metabolites in one biological sample and therefore draw “a modern portrait of metabolomics.” This review describes basic characteristics of these methods, ways of metabolite separation, and analysis of metabolites by mass spectrometry. The examples shown in this review, allow to estimate these methods and to compare their advantages and disadvantages. Besides that, we consider the methods, which are of the most frequent use in metabolomics; these include the methods for data processing and the required resources, such as software for mass spectra processing and metabolite search database. In the conclusion, general suggestions for successful metabolomic experiments are given.     

PTR-ToF-MS and data mining methods: a new tool for fruit metabolomics

Proton Transfer Reaction-Mass Spectrometry (PTR-MS) in its recently developed implementation based on a time-of-flight mass spectrometer (PTR-ToF-MS) has been evaluated as a possible tool for rapid non-destructive investigation of the volatile compounds present in the metabolome of apple cultivars and clones. Clone characterization is a cutting-edge problem in technical management and royalty application, not only for apple, aiming at unveiling real properties which differentiate the mutated individuals. We show that PTR-ToF-MS coupled with multivariate and data mining methods may successfully be employed to obtain accurate varietal and clonal apple fingerprint. In particular, we studied the VOC emission profile of five different clones belonging to three well known apple cultivars, such as ??Fuji??, ??Golden Delicious?? and ??Gala??. In all three cases it was possible to set classification models which can distinguish all cultivars and some of the clones considered in this study. Furthermore, in the case of ??Gala?? we also identified estragole and hexyl 2-methyl butanoate contributing to such clone characterization. Beside its applied relevance, no data on the volatile profiling of apple clones are available so far, our study indicates the general viability of a metabolomic approach for volatile compounds in fruit based on rapid PTR-ToF-MS fingerprinting.     

Terpenoid metabolic profiling analysis of transgenic Artemisia annua L. by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry

Amorpha-4,11-diene synthase (ADS) is a very important enzyme which catalyzes the committed step of artemisinin biosynthesis. In this work, two lines of transgenic Artemisia annua L. which ADS was over-expressed (line A9) and suppressed (line Amsi), respectively, were utilized. And the transgenic line GUS with β-Glucuronidase gene was regarded as the control. Their terpenoid metabolic profiling was investigated by using GC × GC–TOFMS. The metabolic profiling method established included simple extraction, two-dimension separation and multivariate analysis. Partial least squares discriminant analysis (PLS-DA) was used to classify two transgenic lines and the control line. Eleven important compounds in classification were identified. Most of them were sesquiterpenoids including monoterpenoid, diterpenoid and four bioprecursors of artemsisnin. Compared with the control, artemisinin and bioprecursors in the line A9 increased as a result of over-expressing ADS. Borneol and phytol also increased in the line A9, but (E)-β-farnesene and germacrene D were reversely altered. The result indicated that over-expression of the ADS affected not only artemisinin biosynthesis, but also the whole metabolic network of terpenoid. Compared with the line A9, no opposite change of artemisinin and related derivatives was observed in the line Amsi, the ADS inhibition had no significant effect on artemisinin biosynthesis in the line Amsi.     

Liquid chromatography–mass spectrometry-based metabolomics for authenticity assessment of fruit juices

High performance liquid chromatography?Cmass spectrometry (HPLC?CMS) technique, employing a hybrid triple quadrupole/linear ion trap (QqQ/LIT) mass analyzer, was used for comprehensive metabolomic fingerprinting of several fruit juices types, prepared from expensive (orange) or relatively low-priced (apple, grapefruit) fruits. Following the automated data mining and pre-treatment step, the suitability of the multivariate HPLC?CMS metabolomic data for authentication, i.e., classification of fruit juice and adulteration detection, was assessed with the use of advanced chemometric tools (principal component analysis, PCA, and linear discrimination analysis, LDA). The LDA classification model, constructed and validated employing a highly variable samples set, was able to reliably detect 15% addition of apple or grapefruit juice to orange juice. In the final stage of this study, high performance liquid chromatography?Cquadrupole?Cquadrupole-time-of-flight mass spectrometry (HPLC?CQqTOFMS) measurements were performed in order to obtain data for identification of pre-selected marker compounds using elemental formula calculation and online databases search.     

Identification of free fatty acids profiling of type 2 diabetes mellitus and exploring possible biomarkers by GC–MS coupled with chemometrics

Free fatty acids (FFAs), which are considered to be closely related with type 2 diabetes mellitus (T2DM), are not only the main energy source as nutrients, but also signaling molecules in insulin secretion. In this study, gas chromatography–mass spectrometry (GC–MS) coupled with two chemometric resolution methods, heuristic evolving latent projections (HELP) and selective ion analysis (SIA), was successfully applied to investigate plasma FFAs profiling of T2DM. Totally, twenty-three FFAs were identified and quantified. The results showed that HELP and SIA methods could be used to effectively handle overlapping peaks of GC–MS data and hence improve the qualitative and quantitative accuracy. Furthermore, a newly proposed competitive adaptive reweighted sampling (CARS) method coupled with partial least squares linear discriminant analysis (PLS-LDA) was introduced to seek the potential biomarkers. Finally, three fatty acids, oleic acid (OLA C18:1n-9), α-linolenic acid (ALA C18:3n-3), and eicosapentaenoic acid (EPA C20:5n-3), were identified as the potential biomarkers of T2DM for their powerful discriminant ability of T2DM patients from healthy controls. The study indicated that GC–MS combining with chemometric methods was a useful strategy to analyze metabolites and further screen the potential biomarkers of T2DM.     

Development of a metabolomic approach based on urine samples and direct infusion mass spectrometry

The analysis of urine by direct infusion mass spectrometry suffers from ion suppression due to its high salt content and inter-sample variability caused by the differences in urine volume between persons. Thus, urine metabolomics requires a careful selection of the sample preparation procedure and a normalization strategy to deal with these problems. Several approaches were tested for metabolomic analysis of urine samples by direct infusion electrospray mass spectrometry (DI–ESI–MS), including solid phase extraction, liquid–liquid extraction, and sample dilution. In addition, normalization of results based on conductivity values and statistical treatment was performed to minimize sample variability. Both urine dilution and solid phase extraction with mixed mode sorbent considerably reduced the salt content in urine, providing comprehensive metabolomic fingerprints. Moreover, statistical data normalization enabled the correction of inter-sample physiological variability, improving the quality of results obtained. Therefore, high-throughput DI–ESI–MS fingerprinting of urine samples can be achieved with simple pretreatment procedures allowing the use of this noninvasive sampling in metabolomics. Finally, the optimized approach was tested in a pilot metabolomic investigation of urine samples from transgenic mice models of Alzheimer’s disease (APP/PS1) in order to illustrate the potential of the methodology.     

Measuring Hordein (Gluten) in Beer – A Comparison of ELISA and Mass Spectrometry

Background

Subjects suffering from coeliac disease, gluten allergy/intolerance must adopt a lifelong avoidance of gluten. Beer contains trace levels of hordeins (gluten) which are too high to be safely consumed by most coeliacs. Accurate measurement of trace hordeins by ELISA is problematic.

Methods

We have compared hordein levels in sixty beers, by sandwich ELISA, with the level determined using multiple reaction monitoring mass spectrometry (MRM-MS).

Results

Hordein levels measured by ELISA varied by four orders of magnitude, from zero (for known gluten-free beers) to 47,000 µg/mL (ppm; for a wheat-based beer). Half the commercial gluten-free beers were free of hordein by MS and ELISA. Two gluten-free and two low-gluten beers had zero ELISA readings, but contained significant hordein levels (p<0.05), or near average (60–140%) hordein levels, by MS, respectively. Six beers gave false negatives, with zero ELISA readings but near average hordein content by MS. Approximately 20% of commercial beers had ELISA readings less than 1 ppm, but a near average hordein content by MS. Several barley beers also contained undeclared wheat proteins.

Conclusions

ELISA results did not correlate with the relative content of hordein peptides determined by MS, with all barley based beers containing hordein. We suggest that mass spectrometry is more reliable than ELISA, as ELISA enumerates only the concentration of particular amino-acid epitopes; this may vary between different hordeins and may not be related to the absolute hordein concentration. MS quantification is undertaken using peptides that are specific and unique, enabling the quantification of individual hordein isoforms. This outlines the problem of relying solely on ELISA determination of gluten in beverages such as beer and highlights the need for the development of new sensitive and selective quantitative assay such as MS.     

Metabolic profiling by ion mobility mass spectrometry (IMMS)

Ion Mobility Mass Spectrometry (IMMS) was evaluated as an analytical method for metabolic profiling. The specific instrument used in these studies was a direct infusion (DI)-electrospray ionization (ESI)—ambient pressure ion mobility spectrometer (APIMS) coupled to a time-of-flight mass spectrometer (TOFMS). The addition of an ion mobility spectrometer to a mass spectrometer had several advantages over direct infusion electrospray mass spectrometry alone. This tandem instrument (ESI-IMMS) added a rapid separation step with high-resolution prior to mass spectrometric analysis of metabolite mixtures without extending sample preparation time or reducing the high through put potential of direct mass spectrometry. Further, IMMS also reduced the baseline noise common with ESI-MS analyses of complex samples and enabled rapid separation of isobaric metabolites. IMMS was used to analyze the metabolome of Escherichia coli (E. coli), containing a collection of extremely diverse chemical compounds including hydrophobic lipids, inorganic ions, volatile alcohols and ketones, amino and non-amino organic acids, and hydrophilic carbohydrates. IMMS data were collected as two-dimensional spectra showing both mobility and mass of each ion detected. Using direct infusion ESI-IMMS of a non-derivatized methanol extract of an E. coli culture, more than 500 features were detected, of which over 200 intracellular metabolites were tentatively assigned as E. coli metabolites. This analytical method also allowed simultaneous separation of isomeric metabolic features.     

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.     

Use of the Local False Discovery Rate for Identification of Metabolic Biomarkers in Rat Urine Following Genkwa Flos-Induced Hepatotoxicity

Metabolomics is concerned with characterizing the large number of metabolites present in a biological system using nuclear magnetic resonance (NMR) and HPLC/MS (high-performance liquid chromatography with mass spectrometry). Multivariate analysis is one of the most important tools for metabolic biomarker identification in metabolomic studies. However, analyzing the large-scale data sets acquired during metabolic fingerprinting is a major challenge. As a posterior probability that the features of interest are not affected, the local false discovery rate (LFDR) is a good interpretable measure. However, it is rarely used to when interrogating metabolic data to identify biomarkers. In this study, we employed the LFDR method to analyze HPLC/MS data acquired from a metabolomic study of metabolic changes in rat urine during hepatotoxicity induced by Genkwa flos (GF) treatment. The LFDR approach was successfully used to identify important rat urine metabolites altered by GF-stimulated hepatotoxicity. Compared with principle component analysis (PCA), LFDR is an interpretable measure and discovers more important metabolites in an HPLC/MS-based metabolomic study.     

Development and optimization of a metabolomic method for analysis of adherent cell cultures

In this investigation, a gas chromatography/mass spectrometry (GC/MS)-based metabolomic protocol for adherent cell cultures was developed using statistical design of experiments. Cell disruption, metabolite extraction, and the GC/MS settings were optimized aiming at a gentle, unbiased, sensitive, and high-throughput metabolomic protocol. Due to the heterogeneity of the metabolome and the inherent selectivity of all analytical techniques, development of unbiased protocols is highly complex. Changing one parameter of the protocol may change the response of many groups of metabolites. In this investigation, statistical design of experiments and multivariate analysis also allowed such interaction effects to be taken into account. The protocol was validated with respect to linear range, precision, and limit of detection in a clonal rat insulinoma cell line (INS-1 832/13). The protocol allowed high-throughput profiling of metabolites covering the major metabolic pathways. The majority of metabolites displayed a linear range from a single well in a 96-well plate up to a 10 cm culture dish. The method allowed a total of 47 analyses to be performed in 24 h.     

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

Introduction

Contemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis.

Objectives

The aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles.

Methods

Nine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates.

Results

Baroni-Urbani–Buser (BUB) and Hawkins–Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis.

Conclusion

Fingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.

     

Global analysis of metabolites in rat and human urine based on gas chromatography/time-of-flight mass spectrometry

Sediment in urine may contain low-molecular-weight compounds that should be included in the analysis. To date, no systematic investigation has addressed this issue. We investigated three primary factors that influence the extraction efficiency of metabolites during preparation of urine samples for metabolomic research: centrifugation, pH, and extraction solvents. Obtained with the use of gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) technique and principal component analysis (PCA), our results indicate that (1) conventional centrifugation causes an apparent loss of some metabolites, indicating that urine samples for metabolomic research should not be centrifuged before procedures are undertaken to recover the metabolites; (2) pH adjustment has a large impact on the recovery of metabolites and is therefore not encouraged; (3) with design of experiment analysis, methanol and water yield the optimal extraction efficiency. Differences between rat and human urine were observed and are discussed. Ninety-nine metabolites identified in rat and human urine are presented. An efficient protocol is proposed for the pretreatment of urine samples.     

Effect of the ethanol content of beer on the heat resistance of a spoilage Lactobacillus

D -values of a heterofermentative beer spoilage lactobacillus were measured at 55°C, 60°C and 65°C in beers containing <0·05% to 4·4% v/v ethanol. Z -values for the different beers varied between 9·17 and 12·13°C. At each temperature an increase in ethanol reduced the measured D -value. The maximum, 5·01 min was observed in alcohol-free beer (<0·05%) at 55°C and the minimum, 0·31 min, at 60°C and 65°C in beer containing 4·4% ethanol. D -values could be increased by prior growth in the presence of ethanol. They could be reduced by adding ethanol to alcohol-free beer or by increasing its hop extract content. The implications for the pasteurization of low-alcohol beers are discussed.     

The application of MANOVA to analyse <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis> metabolomic data from factorially designed experiments

Metabolomic technologies produce complex multivariate datasets and researchers are faced with the daunting task of extracting information from these data. Principal component analysis (PCA) has been widely applied in the field of metabolomics to reduce data dimensionality and for visualising trends within the complex data. Although PCA is very useful, it cannot handle multi-factorial experimental designs and, often, clear trends of biological interest are not observed when plotting various PC combinations. Even if patterns are observed, PCA provides no measure of their significance. Multivariate analysis of variance (MANOVA) applied to these PCs enables the statistical evaluation of main treatments and, more importantly, their interactions within the experimental design. The power and scope of MANOVA is demonstrated through two different factorially designed metabolomic investigations using Arabidopsis ethylene signalling mutants and their wild-type. One investigation has multiple experimental factors including challenge with the economically important pathogen Botrytis cinerea and also replicate experiments, while the second has different sample preparation methods and one level of replication ‘nested’ within the design. In both investigations there are specific factors of biological interest and there are also factors incorporated within the experimental design, which affect the data. The versatility of MANOVA is displayed by using data from two different metabolomic techniques; profiling using direct injection mass spectroscopy (DIMS) and fingerprinting using fourier transform infra-red (FT-IR) spectroscopy. MANOVA found significant main effects and interactions in both experiments, allowing a more complete and comprehensive interpretation of the variation within each investigation, than with PCA alone. Canonical variate analysis (CVA) was applied to investigate these effects and their biological significance. In conclusion, the application of MANOVA followed by CVA provided extra information than PCA alone and proved to be a valuable statistical addition in the overwhelming task of analysing metabolomic data.     

Plasma fatty acid metabolic profile coupled with uncorrelated linear discriminant analysis to diagnose and biomarker screening of type 2 diabetes and type 2 diabetic coronary heart diseases

Type 2 diabetes mellitus (T2DM) and type 2 diabetic coronary heart diseases (T2DM–CHD) are directly associated with metabolism disorder of lipid. In the present study, GC–MS followed by multivariate statistical analysis has been successfully applied to plasma free fatty acid metabolic profiling in T2DM and T2DM–CHD. Because principal component analysis and partial least squares-linear discriminant analysis both failed to the class separation among T2DM, T2DM–CHD, and control, uncorrelated linear discriminant analysis (ULDA) was proposed and successfully discriminated these three groups. The predictive correct rates were 81.03%, 85.37%, 88.89% for control and T2DM, control and T2DM–CHD, T2DM and T2DM–CHD, respectively. Furthermore, three potential biomarkers were screened. ULDA are much more efficient than PCA and PLS for discrimination analysis of complex data set. It is undoubtedly that such newly multivariate analysis method will promote and widen the application of metabonome analysis in disease clinical diagnosis.     

Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined ultra-performance liquid chromatography/mass spectrometry: From extraction to data analysis

The investigation presented here describes a protocol designed to perform high-throughput metabolic profiling analysis on human blood plasma by ultra-performance liquid chromatography/mass spectrometry (UPLC/MS). To address whether a previous extraction protocol for gas chromatography (GC)/MS-based metabolic profiling of plasma could be used for UPLC/MS-based analysis, the original protocol was compared with similar methods for extraction of low-molecular-weight compounds from plasma via protein precipitation. Differences between extraction methods could be observed, but the previously published extraction method was considered the best. UPLC columns with three different stationary phases (C8, C18, and phenyl) were used in identical experimental runs consisting of a total of 60 injections of extracted male and female plasma samples. The C8 column was determined to be the best for metabolic profiling analysis on plasma. The acquired UPLC/MS data of extracted male and female plasma samples was subjected to principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA). Furthermore, a strategy for compound identification was applied here, demonstrating the strength of high-mass-accuracy time-of-flight (TOF)/MS analysis in metabolic profiling.     

Lipidomics focusing on serum polar lipids reveals species dependent stress resistance of fish under tropical storm

The serum polar lipid metabolic changes for two common cage-cultured fishes, yellow coraker Pseudosciaena crocea and Japanese seabass Lateolabrax japonicus, after tropical storm attack have been studied by ultra-performance liquid chromatography—quadrupole-time of flight mass spectrometry (UPLC-qTOF-MS). The full scan mass spectrometry combined with principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) indicated that yellow croaker underwent significant chemico-physiological changes during the recovery process, whereas Japanese seabass did not show such noticeable time-dependent consistent metabolites change patterns. Further identification of the metabolite biomarkers showed the increase of phosphatidylcholine with high unsaturated fatty acid and lysophospholipids, and the decrease of phosphatidylcholine with saturated fatty acids and plasmologens, which indicated the need of energy supplement and successive stressful inflammation. The increase of taurocholic acid and decrease of cortol could be regarded as the physiological alleviation measure during the recovery period. This is the first metabolomic study to tackle the fish physiological response for the complex environmental changes, and demonstrated that lipidomics is an effective analytical tool for predicting the stress resistance of fish to ultra uncontrolled environmental stress.