Journal of Chromatography B (v.966, #C)
Preface by Georgios Theodoridis (vii-viii).
Editorial Board (i).
LC–MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives by Helen G. Gika; Ian D. Wilson; Georgios A. Theodoridis (1-6).
The present review aims to critically discuss some of the major problems and limitations of LC–MS based metabolomics as experienced from an analytical chemistry standpoint. Metabolomics offers distinct advantages to a variety of life sciences. Continuous development of the field has been realised due to intensive efforts from a great many scientists from widely divergent backgrounds and research interests as demonstrated by the contents of this special issue. The aim of this commentary is to describe current hindrances to field's progress, (some unique to metabolomics, some common with other omics fields or with conventional targeted bioanalysis) to propose some potential solutions to overcome these constraints and to provide a future perspective for likely developments in the field.
Keywords: Metabolomics; Metabonomics; Standardisation; Quality control; Mass spectrometry;
Genome-enabled plant metabolomics by Takayuki Tohge; Leonardo Perez de Souza; Alisdair R. Fernie (7-20).
The grand challenge currently facing metabolomics is that of comprehensitivity whilst next generation sequencing and advanced proteomics methods now allow almost complete and at least 50% coverage of their respective target molecules, metabolomics platforms at best offer coverage of just 10% of the small molecule complement of the cell. Here we discuss the use of genome sequence information as an enabling tool for peak identity and for translational metabolomics. Whilst we argue that genome information is not sufficient to compute the size of a species metabolome it is highly useful in predicting the occurrence of a wide range of common metabolites. Furthermore, we describe how via gene functional analysis in model species the identity of unknown metabolite peaks can be resolved. Taken together these examples suggest that genome sequence information is current (and likely will remain), a highly effective tool in peak elucidation in mass spectral metabolomics strategies.
Keywords: Plant metabolism; Genomics; Metabolomics; Secondary metabolite;
Chemical and technical challenges in the analysis of central carbon metabolites by liquid-chromatography mass spectrometry by David Siegel; Hjalmar Permentier; Dirk-Jan Reijngoud; Rainer Bischoff (21-33).
This review deals with chemical and technical challenges in the analysis of small-molecule metabolites involved in central carbon and energy metabolism via liquid-chromatography mass-spectrometry (LC–MS). The covered analytes belong to the prominent pathways in biochemical carbon oxidation such as glycolysis or the tricarboxylic acid cycle and, for the most part, share unfavorable properties such as a high polarity, chemical instability or metal-affinity. The topic is introduced by selected examples on successful applications of metabolomics in the clinic. In the core part of the paper, the structural features of important analyte classes such as nucleotides, coenzyme A thioesters or carboxylic acids are linked to “problematic hotspots” along the analytical chain (sample preparation and—storage, separation and detection). We discuss these hotspots from a chemical point of view, covering issues such as analyte degradation or interactions with metals and other matrix components. Based on this understanding we propose solutions wherever available. A major notion derived from these considerations is that comprehensive carbon metabolomics inevitably requires multiple, complementary analytical approaches covering different chemical classes of metabolites.
Keywords: Central carbon metabolism; Energy metabolism; Chemical degradation; Metal interaction;
Annotation of the human serum metabolome by coupling three liquid chromatography methods to high-resolution mass spectrometry by Samia Boudah; Marie-Françoise Olivier; Sandrine Aros-Calt; Lydie Oliveira; François Fenaille; Jean-Claude Tabet; Christophe Junot (34-47).
This work aims at evaluating the relevance and versatility of liquid chromatography coupled to high resolution mass spectrometry (LC/HRMS) for performing a qualitative and comprehensive study of the human serum metabolome. To this end, three different chromatographic systems based on a reversed phase (RP), hydrophilic interaction chromatography (HILIC) and a pentafluorophenylpropyl (PFPP) stationary phase were used, with detection in both positive and negative electrospray modes. LC/HRMS platforms were first assessed for their ability to detect, retain and separate 657 metabolite standards representative of the chemical families occurring in biological fluids. More than 75% were efficiently retained in either one LC-condition and less than 5% were exclusively retained by the RP column. These three LC/HRMS systems were then evaluated for their coverage of serum metabolome. The combination of RP, HILIC and PFPP based LC/HRMS methods resulted in the annotation of about 1328 features in the negative ionization mode, and 1358 in the positive ionization mode on the basis of their accurate mass and precise retention time in at least one chromatographic condition. Less than 12% of these annotations were shared by the three LC systems, which highlights their complementarity. HILIC column ensured the greatest metabolome coverage in the negative ionization mode, whereas PFPP column was the most effective in the positive ionization mode. Altogether, 192 annotations were confirmed using our spectral database and 74 others by performing MS/MS experiments. This resulted in the formal or putative identification of 266 metabolites, among which 59 are reported for the first time in human serum.
Keywords: Liquid chromatography; High resolution mass spectrometry; Tandem mass spectrometry; Metabolite identification; Metabolome annotation; Serum; Metabolomics;
Studies of metabolite–protein interactions: A review by Ryan Matsuda; Cong Bi; Jeanethe Anguizola; Matthew Sobansky; Elliott Rodriguez; John Vargas Badilla; Xiwei Zheng; Benjamin Hage; David S. Hage (48-58).
The study of metabolomics can provide valuable information about biochemical pathways and processes at the molecular level. There have been many reports that have examined the structure, identity and concentrations of metabolites in biological systems. However, the binding of metabolites with proteins is also of growing interest. This review examines past reports that have looked at the binding of various types of metabolites with proteins. An overview of the techniques that have been used to characterize and study metabolite–protein binding is first provided. This is followed by examples of studies that have investigated the binding of hormones, fatty acids, drugs or other xenobiotics, and their metabolites with transport proteins and receptors. These examples include reports that have considered the structure of the resulting solute–protein complexes, the nature of the binding sites, the strength of these interactions, the variations in these interactions with solute structure, and the kinetics of these reactions. The possible effects of metabolic diseases on these processes, including the impact of alterations in the structure and function of proteins, are also considered.
Keywords: Metabolomics; Drug–protein interactions; Hormone–protein interactions; Fatty acid–protein interactions; Xenobiotic–protein interactions; Protein modification;
Metabolome analysis for discovering biomarkers of gastroenterological cancer by Makoto Suzuki; Shin Nishiumi; Atsuki Matsubara; Takeshi Azuma; Masaru Yoshida (59-69).
Improvements in analytical technologies have made it possible to rapidly determine the concentrations of thousands of metabolites in any biological sample, which has resulted in metabolome analysis being applied to various types of research, such as clinical, cell biology, and plant/food science studies. The metabolome represents all of the end products and by-products of the numerous complex metabolic pathways operating in a biological system. Thus, metabolome analysis allows one to survey the global changes in an organism's metabolic profile and gain a holistic understanding of the changes that occur in organisms during various biological processes, e.g., during disease development. In clinical metabolomic studies, there is a strong possibility that differences in the metabolic profiles of human specimens reflect disease-specific states. Recently, metabolome analysis of biofluids, e.g., blood, urine, or saliva, has been increasingly used for biomarker discovery and disease diagnosis. Mass spectrometry-based techniques have been extensively used for metabolome analysis because they exhibit high selectivity and sensitivity during the identification and quantification of metabolites. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Furthermore, the findings of studies that attempted to discover biomarkers of gastroenterological cancer are also outlined. Finally, we discuss metabolome analysis-based disease diagnosis.
Keywords: Metabolome analysis; Metabolomics; Serum; Cancer; Biomarker; Diagnosis;
Identification of organic acids as potential biomarkers in the urine of autistic children using gas chromatography/mass spectrometry by Joanna Kałużna-Czaplińska; Ewa Żurawicz; Wiktoria Struck; Michał Markuszewski (70-76).
There is a need to identify metabolic phenotypes in autism as they might each require unique approaches to prevention. Biological markers can help define autism subtypes and reveal potential therapeutic targets. The aim of the study was to identify alterations of small molecular weight compounds and to find potential biomarkers. Gas chromatography/mass spectrometry was employed to evaluate major metabolic changes in low molecular weight urine metabolites of 14 children with autism spectrum disorders vs. 10 non-autistic subjects. The results prove the usefulness of an identified set of 21 endogenous compounds (including 14 organic acids), whose levels are changed in diseased children. Gas chromatography/mass spectrometry method combined with multivariate statistical analysis techniques provide an efficient way of depicting metabolic perturbations of diseases, and may potentially be applicable as a novel strategy for the noninvasive diagnosis and treatment of autism.
Keywords: Organic acids; Gas chromatography–mass spectrometry; Biomarkers; Autism; Principal component analysis;
Integrated enrichment analysis and pathway-centered visualization of metabolomics, proteomics, transcriptomics, and genomics data by using the InCroMAP software by Johannes Eichner; Lars Rosenbaum; Clemens Wrzodek; Hans-Ulrich Häring; Andreas Zell; Rainer Lehmann (77-82).
In systems biology, the combination of multiple types of omics data, such as metabolomics, proteomics, transcriptomics, and genomics, yields more information on a biological process than the analysis of a single type of data. Thus, data from different omics platforms is usually combined in one experimental setup to obtain insight into a biological process or a disease state. Particularly high accuracy metabolomics data from modern mass spectrometry instruments is currently more and more integrated into biological studies. Reflecting this trend, we extended InCroMAP, a data integration, analysis and visualization tool for genomics, transcriptomics, and proteomics data. Now, the tool is able to perform an integrated enrichment analysis and pathway-based visualization of multi-omics data and thus, it is suitable for the evaluation of comprehensive systems biology studies.
Keywords: Metabolomics; Transcriptomics; Proteomics; Genomics; Enrichment analysis; Pathway visualization;
Construction of a metabolome library for transcription factor-related single gene mutants of Saccharomyces cerevisiae by Zanariah Hashim; Shao Thing Teoh; Takeshi Bamba; Eiichiro Fukusaki (83-92).
Transcription factors (TFs) play an important role in gene regulation, providing control for cells to adapt to ever changing environments and different physiological states. Although great effort has been taken to study TFs through DNA–protein binding and microarray gene expression experiments, the understanding of transcriptional regulation is still lacking, due to lack of information that links TF regulatory events and final phenotypic change. Here, we focused on metabolites as the final readouts of gene transcription process. We performed metabolite profiling of 154 Saccharomyces cerevisiae's single gene knockouts each defective in a gene encoding transcription factor and built a metabolome library consists of 84 metabolites with good reproducibility. Using the metabolome dataset, we obtained significant correlations and identified differential strains that exhibit altered metabolism compared to control. This work presents a novel metabolome dataset library which will be invaluable for researchers working on transcriptional regulation and yeast biology in general.
Keywords: Metabolomics; Transcription factors; Hierarchical clustering; Yeast Saccharomyces cerevisiae;
Correlated measurement error hampers association network inference by Mateusz Kaduk; Huub C.J. Hoefsloot; Daniel J. Vis; Theo Reijmers; Jan van der Greef; Age K. Smilde; Margriet M.W.B. Hendriks (93-99).
Modern chromatography-based metabolomics measurements generate large amounts of data in the form of abundances of metabolites. An increasingly popular way of representing and analyzing such data is by means of association networks. Ideally, such a network can be interpreted in terms of the underlying biology. A property of chromatography-based metabolomics data is that the measurement error structure is complex: apart from the usual (random) instrumental error there is also correlated measurement error. This is intrinsic to the way the samples are prepared and the analyses are performed and cannot be avoided. The impact of correlated measurement errors on (partial) correlation networks can be large and is not always predictable. The interplay between relative amounts of uncorrelated measurement error, correlated measurement error and biological variation defines this impact. Using chromatography-based time-resolved lipidomics data obtained from a human intervention study we show how partial correlation based association networks are influenced by correlated measurement error. We show how the effect of correlated measurement error on partial correlations is different for direct and indirect associations. For direct associations the correlated measurement error usually has no negative effect on the results, while for indirect associations, depending on the relative size of the correlated measurement error, results can become unreliable. The aim of this paper is to generate awareness of the existence of correlated measurement errors and their influence on association networks. Time series lipidomics data is used for this purpose, as it makes it possible to visually distinguish the correlated measurement error from a biological response. Underestimating the phenomenon of correlated measurement error will result in the suggestion of biologically meaningful results that in reality rest solely on complicated error structures. Using proper experimental designs that allow for the quantification of the size of correlated and uncorrelated errors, can help to identify suspicious connections in association networks constructed from (partial) correlations.
Keywords: Measurement error; Metabolomics; Measurement design; Figures-of-merit;
A modified k-TSP algorithm and its application in LC–MS-based metabolomics study of hepatocellular carcinoma and chronic liver diseases by Xiaohui Lin; Jiuchong Gao; Lina Zhou; Peiyuan Yin; Guowang Xu (100-108).
In systems biology, the ability to discern meaningful information that reflects the nature of related problems from large amounts of data has become a key issue. The classification method using top scoring pairs (TSP), which measures the features of a data set in pairs and selects the top ranked feature pairs to construct the classifier, has been a powerful tool in genomics data analysis because of its simplicity and interpretability. This study examined the relationship between two features, modified the ranking criteria of the k-TSP method to measure the discriminative ability of each feature pair more accurately, and correspondingly, provided an improved classification procedure. Tests on eight public data sets showed the validity of the modified method. This modified k-TSP method was applied to our serum metabolomics data derived from liquid chromatography-mass spectrometry analysis of hepatocellular carcinoma and chronic liver diseases. Based on the 27 selected feature pairs, HCC and chronic liver diseases were accurately distinguished using the principal component analysis, and certain profound metabolic disturbances related to liver disease development were revealed by the feature pairs.
Keywords: TSP; Metabolomics; Liver diseases; Feature selection; Top scoring pairs; LC–MS;
metaMS: An open-source pipeline for GC–MS-based untargeted metabolomics by Ron Wehrens; Georg Weingart; Fulvio Mattivi (109-116).
Untargeted metabolomics are rapidly becoming an important tool for studying complex biological samples. Gas chromatography–mass spectrometry (GC–MS) is the most widely used analytical technology for metabolomic analysis of compounds that are volatile or can be chemically derivatised into volatile compounds. Unfortunately, data processing and analysis are not straightforward and the field is dominated by vendor-supplied software that does not always allow easy integration for large laboratories with different instruments. This paper presents an open-source pipeline for high-throughput GC–MS data processing, written in the R language and available as package metaMS. It features rapid annotation using in-house databases, and also provides support for building and validating such databases. The results are presented in simple-to-use tables, summarising the relative concentrations of identified compounds and unknowns in all samples. The use of the pipeline is illustrated using three experimental data sets.
Keywords: Metabolomics; GC–MS; Annotation; Open-source software; Volatiles;
Metabolomics using GC–TOF–MS followed by subsequent GC–FID and HILIC–MS/MS analysis revealed significantly altered fatty acid and phospholipid species profiles in plasma of smokers by Daniel C. Müller; Christian Degen; Gerhard Scherer; Gerhard Jahreis; Reinhard Niessner; Max Scherer (117-126).
Mass spectrometry is an ideal tool for investigations of the metabolome in human plasma. To investigate the impact of smoking on the human metabolome, we performed an untargeted metabolic fingerprinting using GC–TOF–MS with EDTA-plasma samples from 25 smokers and 25 non-smokers. The observed elevated levels in the monounsaturated fatty acids (MUFAs) in smokers were verified by a targeted analysis using GC–FID, which revealed also significantly alterations in saturated and polyunsaturated fatty acids in smokers (p < 0.05, Mann–Whitney U test). Since the main fraction of fatty acids in plasma is esterified to phospholipids, we analyzed phosphatidylcholine (PC) and phosphatidylethanolamine (PE) species composition in the plasma samples of the same subjects. The profiles of 39 PC and 40 PE species were analyzed with a newly developed and validated HILIC–ESI–MS/MS method. We were able to baseline separate the two lipid classes (PC from PE) by maintaining co-elution of individual lipid species of each class. The method shows a linear range from 0.5 μM to 2000 μM and an inter- and intraday coefficient of variation (CV) < 20% across all analytes. Application of the validated method to the plasma samples of smokers and non-smokers, derived from a diet-controlled smoking study, revealed significantly elevated levels of PC and PE species containing MUFAs in smokers.In summary, we could demonstrate that there is a significantly altered total fatty acid profile, with increased MUFAs, in the plasma of smokers compared to non-smokers. Results obtained with the new HILIC–MS/MS method indicate that the altered fatty acid profile is also reflected in the PC and PE profile of smokers.
Keywords: Metabolomics; HILIC–MS/MS; Phospholipids; Plasma fatty acids; Monounsaturated fatty acids; Stearoyl Coenzyme A desaturase 1;
GC–MS analysis of blood for the metabonomic investigation of the effects of physical exercise and allopurinol administration on rats by Alexandros Pechlivanis; Anastasia Chrysovalantou Chatziioannou; Aristidis S. Veskoukis; Dimitrios Kouretas; Vassilis Mougios; Georgios A. Theodoridis (127-131).
Exhaustive exercise is a generator of free radicals and reactive species in mammals. Allopurinol is a known inhibitor of xanthine oxidase, a source of free radicals during exercise. In this study, the influence of allopurinol on the metabolic profile of blood plasma of rats that had undergone exhaustive swimming was investigated by GC–MS. Rats were divided into four groups: (i) placebo administration, no exercise; (ii) placebo administration followed by exercise until exhaustion; (iii) allopurinol administration, no exercise; and (iv) allopurinol administration followed by exercise until exhaustion. Samples obtained following the aforementioned treatments were analyzed on GC–MS after two-step derivatization (methoxymation and silylation). GC–MS analysis in full scan acquisition achieved the quantitation of 86 metabolites in 45 min. GC–MS data were analyzed using univariate and multivariate statistical analysis methods. Safe classification/prediction of the samples was accomplished according to exercise and allopurinol administration. Separation of the study groups according to exercise was mainly due to lactic acid, pyruvic acid, 2-hydroxybutyric acid, uracil, oxalic acid, pyroglutamic acid and stearic acid (p < 0.05). Separation according to allopurinol administration was mainly due to compounds of the purine catabolic pathway and amino acids. Allopurinol administration was not found to modulate the metabolic responses to exercise.
Keywords: Allopurinol; Metabonomics; Metabolomics; GC–MS; Physical exercise;
Quantitative metabolic profiling of grape, apple and raspberry volatile compounds (VOCs) using a GC/MS/MS method by Urska Vrhovsek; Cesare Lotti; Domenico Masuero; Silvia Carlin; Georg Weingart; Fulvio Mattivi (132-139).
Nowadays the trend in analytical chemistry is clearly towards the creation of multiple methods with extended coverage, enabling the determination of many different classes of compounds in a single analysis in which virtually all classes of different compounds are included in a single run. The aim of this study was to develop and validate a versatile and selective GC/MS/MS method for metabolite profiling of volatile compounds in apples, raspberries and grapes. Validation of the method was performed in terms of the limit of detection, limit of quantification, linearity range, and inter and intraday precision. Confirmation of the identity of the compounds in samples was carried out by checking compliance of the q/Q ratio of samples and reference standards. The multiple reaction monitoring with selection of two transition ions, one for quantification and one for confirmation, provided excellent selectivity and sensitivity, using the q/Q ratio as a confirmatory parameter. A multi target method was developed and validated for the simultaneous quantification and confirmation of 160 volatile compounds of raspberries, apples and grapes. The main classes were esters (42), alcohols (32), monoterpenes (31), aldehydes (17), ketones (12), norisoprenoids (8), acids (8), sesquiterpenes (7), pyrazines (3) and ethers (1) allowing the detection and quantification of 69 compounds in apples, 122 in grapes and 42 in raspberries. Moreover, the method developed can be easily extended to volatile compounds in other fruits and can therefore be widely used for quantification/profiling studies in the field of fruit aroma.
Keywords: GC tandem mass spectrometry; VOC; Fruits; Targeted metabolomics;
Habitual dietary intake impacts on the lipidomic profile by A. O’Gorman; C. Morris; M. Ryan; C.M. O’Grada; H.M. Roche; E.R. Gibney; M.J. Gibney; L. Brennan (140-146).
Reliable dietary assessments are essential when attempting to understand the complex links between diet and health. Traditional methods for collecting dietary exposure can be unreliable, therefore there is an increasing interest in identifying biomarkers to provide a more accurate measurement. Metabolomics is a technology that offers great promise in this area. The aim of this study was to use a multivariate statistical strategy to link lipidomic patterns with dietary data in an attempt to identify dietary biomarkers. We assessed the relationship between lipidomic profiles and dietary data in volunteers (n = 34) from the Metabolic Challenge Study (MECHE). Principal component analysis (PCA), linear regression and receiver operating characteristic (ROC) analysis were used to (1) reduce the lipidomic data into lipid patterns (LPs), (2) investigate relationships between these patterns and dietary data and (3) identify biomarkers of dietary intake. Our study identified a total of 6 novel LPs. LP1 was highly predictive of dietary fat intake (area under the curve AUC = 0.82). A random forest (RF) classification model used to discriminate between low and high consumers resulted with an error rate of >10%, with a panel of six metabolites identified as the most predictive. LP4 was highly predictive of alcohol intake (AUC = 0.81) with lysophosphatidylcholine alkyl C18:0 (LPCeC18:0) identified as a potential biomarker of alcohol consumption. LP6 had a reasonably good ability to predict dietary fish intake (AUC = 0.76), with lysophosphatidylethanolamine acyl C18:2 (LPEaC18:2) phoshatidylethanolamine diaclyl C38:4 (PEaaC38:4) identified as potential biomarkers. The identification of these LPs and specific biomarkers will help in better classifying a persons dietary intake and in turn will improve the assessment of the relationship between diet and disease. Linking these LPs and specific biomarkers with health parameters will be an important future step.
Keywords: Lipidomics; Dietary assessment; Dietary biomarkers; Lipid patterns;
Serum metabolic profiling study of lung cancer using ultra high performance liquid chromatography/quadrupole time-of-flight mass spectrometry by Yanjie Li; Xue Song; Xinjie Zhao; Lijuan Zou; Guowang Xu (147-153).
Lung cancer is currently the leading cause of cancer-related mortality worldwide. It is, therefore, important to enhance understanding and add a new auxiliary detection tool of lung cancer. In this work, serum metabolic characteristics of lung cancer were investigated with a non-targeted metabolomics method. The metabolic profiling of 23 patients with lung cancer and 23 healthy controls were analyzed using ultra high performance liquid chromatography/quadrupole time of flight mass spectrometry (UPLC/Q-TOF MS). Partial least squares discriminant analysis (PLS-DA) model of the metabolic data allowed the clear separation of the lung cancer patients from the healthy controls. In total, 27 differential metabolites were identified, which were mostly related to the perturbation of lipid metabolism, including choline, free fatty acids, lysophosphatidylcholines, etc. Choline and linoleic acid were defined as one combinational biomarker using binary logistic regression, which was supported by the validation with a smaller sample-set (9 patients and 9 healthy controls). These findings show that LC/MS-based serum metabolic profiling has potential application in complementary identification of lung cancer patients, and could be a powerful tool for cancer research.
Keywords: Lung cancer; Metabolomics; Metabolic profiling; Potential biomarker;
Detection of hepatocellular carcinoma in hepatitis C patients: Biomarker discovery by LC–MS by Jeremiah Bowers; Emma Hughes; Nicholas Skill; Mary Maluccio; Daniel Raftery (154-162).
Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n = 37) and HCV (n = 21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC–MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p < 0.05) and sufficiently (FC ≤ 0.7 or FC ≥ 1.3) showed good performance using p-values alone; the best of these PLS-DA models was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC–MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between high-risk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLS-DA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application.
Keywords: Metabolomics; metabolic profiling; Hepatocellular carcinoma; Hepatitis C; Liver cancer; HPLC–MS; LC–MS; Early cancer detection; Biomarker;
Metabolic profiling study of early and late recurrence of hepatocellular carcinoma based on liquid chromatography-mass spectrometry by Lina Zhou; Yuan Liao; Peiyuan Yin; Zhongda Zeng; Jia Li; Xin Lu; Limin Zheng; Guowang Xu (163-170).
The objectives of this pilot study were to predict early postoperative recurrence in hepatocellular carcinoma (HCC) patients based on metabolic features and to explore the related metabolic disturbances. Liquid chromatography-mass spectrometry-based metabolic profiling was performed on the plasma of 18 late recurrent and 22 early recurrent HCC patients. Metabolic differences were found to be related to amino acid, bile acid, cholesterol, fatty acid, phospholipid and carbohydrate metabolism. Bile acids, steroids and fatty acids showed significant variation in the early recurrent HCC group compared to the late recurrence group. Decreased levels of polyunsaturated eicosapentaenoic acid, docosahexaenoic acid and linolenic acid were found to be specific metabolic features for early recurrence. With the combination of methionine, GCDCA and cholesterol sulfate, 85% of the early recurrent HCCs can be predicted correctly with the corresponding area under the curve (AUC) equal to 0.95 in the training set, and 80% of the early recurrent HCCs can be predicted correctly with the corresponding AUC equal to 0.91 in the test set.
Keywords: Metabolic profiling; Hepatocellular carcinoma; Curative resection; Early recurrence; Metabolomics;
LC–MS/MS analysis of uncommon paracetamol metabolites derived through in vitro polymerization and nitration reactions in liquid nitrogen by Arne Trettin; Jens Jordan; Dimitrios Tsikas (171-178).
Paracetamol (acetaminophen, APAP) is a commonly used analgesic drug. Known paracetamol metabolites include the glucuronide, sulfate and mercapturate. N-Acetyl-benzoquinonimine (NAPQI) is considered the toxic intermediate metabolite of paracetamol. In vitro and in vivo studies indicate that paracetamol is also metabolized to additional poorly characterized metabolites. For example, metabolomic studies in urine samples of APAP-treated mice revealed metabolites such as APAP-sulfate-APAP and APAP-S-S-APAP in addition to the classical phase II metabolites. Here, we report on the development and application of LC–MS and LC–MS/MS approaches to study reactions of unlabelled and 2H-labelled APAP with unlabelled and 15N-labelled nitrite in aqueous phosphate buffers (pH 7.4) upon their immersion into liquid nitrogen (−196 °C). In mechanistic studies, these reactions were also studied in aqueous buffer prepared in 18O-labelled water. LC–MS and LC–MS/MS analyses were performed on a reverse-phase material (C18) using gradient elution (2 mM ammonium acetate/acetonitrile), in positive and negative electrospray mode. We identified a series of APAP metabolites including di-, tri- and tetra-APAP, mono- and di-nitro-APAP and nitric ester of di-APAP. Our study indicates that nitrite induces oxidation, i.e., polymerization and nitration of APAP, when buffered APAP/nitrite solutions are immersed into liquid nitrogen. These reactions are specific for nitrite with respect to nitrate and do not proceed via intermediate formation of NAPQI. Potassium ions and physiological saline but not thiols inhibit nitrite- and shock-freeze-induced reactions of paracetamol. The underlying mechanism likely involves in situ formation of •NO2 radicals from nitrite secondary to profound pH reduction (down to pH 1) and disproportionation. Polymeric paracetamol species can be analyzed as pentafluorobenzyl derivatives by LC–MS but not by GC–MS.
Keywords: Derivatization; Liquid nitrogen; Nitrite; Polymerization; Sodium phosphate; Stable isotopes;
Liquid chromatography time of flight mass spectrometry based environmental metabolomics for the analysis of Pseudomonas putida Bacteria in potable water by Konstantinos A. Kouremenos; David J. Beale; Henrik Antti; Enzo A. Palombo (179-186).
Water supply biofilms have the potential to harbour waterborne diseases, accelerate corrosion, and contribute to the formation of tuberculation in metallic pipes. One particular species of bacteria known to be found in the water supply networks is Pseudomonas sp., with the presence of Pseudomonas putida being isolated to iron pipe tubercles. Current methods for detecting and analysis pipe biofilms are time consuming and expensive. The application of metabolomics techniques could provide an alternative method for assessing biofilm risk more efficiently based on bacterial activity. As such, this paper investigates the application of metabolomic techniques and provides a proof-of-concept application using liquid chromatography coupled with time-of-flight mass spectrometry (LC–ToF-MS) to three biologically independent P. putida samples, across five different growth conditions exposed to solid and soluble iron (Fe). Analysis of the samples in +ESI and −ESI mode yielded 887 and 1789 metabolite features, respectively. Chemometric analysis of the +ESI and −ESI data identified 34 and 39 significant metabolite features, respectively, where features were considered significant if the fold change was greater than 2 and obtained a p-value less than 0.05. Metabolite features were subsequently identified according to the Metabolomics Standard Initiative (MSI) Chemical Analysis Workgroup using analytical standards and standard online LC–MS databases. Possible markers for P. putida growth, with and without being exposed to solid and soluble Fe, were identified from a diverse range of different chemical classes of metabolites including nucleobases, nucleosides, dipeptides, tripeptides, amino acids, fatty acids, sugars, and phospholipids.
Keywords: Environmental metabolomics; Liquid chromatography; Time-of-flight mass spectrometry; Pseudomonas putida; Biofilms; Chemometrics;
Chiral amino acid analysis of Japanese traditional Kurozu and the developmental changes during earthenware jar fermentation processes by Yurika Miyoshi; Masanobu Nagano; Shoto Ishigo; Yusuke Ito; Kazunori Hashiguchi; Naoto Hishida; Masashi Mita; Wolfgang Lindner; Kenji Hamase (187-192).
Enantioselective amino acid metabolome analysis of the Japanese traditional black vinegars (amber rice vinegar, Kurozu) was performed using two-dimensional high-performance liquid chromatography combining a microbore-monolithic ODS column and narrowbore-enantioselective columns. d-Amino acids, the enantiomers of widely observed l-amino acids, are currently paid attention as novel physiologically active substances, and the foodstuffs and beverages containing high amounts of d-amino acids are the subjects of interest. In the present study, the amino acid enantiomers were determined by two-dimensional HPLC techniques after pre-column fluorescence derivatization with 4-fluoro-7-nitro-2,1,3-benzoxadiazole. In the first dimension, the amino acid enantiomers are separated as their d plus l mixtures by the reversed-phase mode, then the d-amino acids and their l-counterparts are separately determined in the second dimension by the enantioselective columns. As a result, large amounts of d-Ala (800–4000 nmol/mL), d-Asp (200–400 nmol/mL) and d-Glu (150–500 nmol/mL) were observed in some of the traditionally produced Kurozu vinegars. Relatively large or small amounts of d-Ser (50–100 nmol/mL), d-Leu (10–50 nmol/mL) and d-allo-Ile (less than 20 nmol/mL) were also present in these samples. Developmental changes in the d-amino acid amounts during the fermentation and aging processes have also been investigated
Keywords: d-Amino acids; Black vinegar (Kurozu); 2D-HPLC; Enantiomer separation; Chiral metabolomics;
Profiling of regioisomeric triacylglycerols in edible oils by supercritical fluid chromatography/tandem mass spectrometry by Jae Won Lee; Toshiharu Nagai; Naohiro Gotoh; Eiichiro Fukusaki; Takeshi Bamba (193-199).
In this study, supercritical fluid chromatography (SFC) coupled with triple quadrupole mass spectrometry was applied to the profiling of several regioisomeric triacylglycerols (TAGs). SFC conditions (column, flow rate, modifier) were optimized for the effective separation of TAGs. In the column test, a triacontyl (C30) silica gel reversed-phase column was selected to separate TAG regioisomers. Multiple reaction monitoring was used to selectively quantify each TAG. Then, the method was used to perform detailed characterization of a diverse array of TAGs in palm and canola oils. Seventy TAGs (C46:0–C60:2) of these oils were successfully analyzed as a result, and twenty isomeric TAG pairs were separated well. In particular, this method provided the fast and high resolution separation of six regioisomeric TAG pairs (PPLn/PLnP, PPL/PLP, PPO/POP, SPLn/SLnP, SPO/SOP, SSO/SOS–stearic acid (S, 18:0), oleic acid (O, 18:1), linoleic acid (L, 18:2), linolenic acid (Ln, 18:3), palmitic acid (P, 16:0)) in a short time (50 min) as compared to high performance liquid chromatography. We were able to demonstrate the utility of this method for the analysis of regioisomeric TAGs in edible oils.
Keywords: Supercritical fluid chromatography; Regioisomers; Triacylglycerol; Triple quadrupole mass spectrometry;
Ultra high resolution SFC–MS as a high throughput platform for metabolic phenotyping: Application to metabolic profiling of rat and dog bile by Michael D. Jones; Paul D. Rainville; Giorgis Isaac; Ian D. Wilson; Norman W. Smith; Robert S. Plumb (200-207).
Ultra high resolution SFC–MS (on sub-2 μm particles) coupled to mass spectrometry has been evaluated for the metabolic profiling of rat and dog bile. The selectivity of the SFC separation differed from that seen in previous reversed-phase UPLC–MS studies on bile, with the order of elution for analytes such as e.g., the bile acids showing many differences. The chromatography system showed excellent stability, reproducibility and robustness with relative standard deviation of less than 1% for retention time obtained over the course of the analysis. SFC showed excellent chromatographic performance with chromatographic peak widths in the order of 3 s at the base of the peak. The use of supercritical fluid carbon dioxide as a mobile phase solvent also reduced the overall consumption of organic solvent by a factor of 3 and also reduced the overall analysis time by a factor of 30% compared to reversed-phase gradient LC. SFC–MS appear complementary to RPLC for the metabolic profiling of complex samples such as bile.
Keywords: Metabolic profiling; Metabonomics; Metabolomics; Bile metabolites; Bile acids; SFC;
Application of PTR-TOF-MS to investigate metabolites in exhaled breath of patients affected by coeliac disease under gluten free diet by Eugenio Aprea; Luca Cappellin; Flavia Gasperi; Filomena Morisco; Vincenzo Lembo; Antonio Rispo; Raffaella Tortora; Paola Vitaglione; Nicola Caporaso; Franco Biasioli (208-213).
Coeliac disease (CD) is a common chronic inflammatory disorder of the small bowel induced in genetically susceptible people by the exposure to gliadin gluten. Even though several tests are available to assist the diagnosis, CD remains a biopsy-defined disorder, thus any non-invasive or less invasive diagnostic tool may be beneficial. The analysis of volatile metabolites in exhaled breath, given its non-invasive nature, is particularly promising as a screening tool of disease in symptomatic or non-symptomatic patients.In this preliminary study the proton transfer reaction time of flight mass spectrometry coupled to a buffered end-tidal on-line sampler to investigate metabolites in the exhaled breath of patients affected by coeliac disease under a gluten free diet was applied. Both H3O+ or NO+ were used as precursor ions. In our investigation no differences were found in the exhaled breath of CD patients compared to healthy controls. In this study, 33 subjects were enrolled: 16 patients with CD, all adhering a gluten free diet, and 17 healthy controls. CD patients did not show any symptom of the disease at the time of breath analysis; thus the absence of discrimination from healthy controls was not surprising.
Keywords: PTR-ToF-MS; Coeliac disease; Breath analysis; Volatile metabolites; Gluten free diet;