Metabonomics and Biomarker Discovery: LC−MS Metabolic Profiling and Constant Neutral Loss Scanning Combined with Multivariate Data Analysis for Mercapturic Acid Analysis

Multivariate classification of rice wines according to ageing time and brand based on amino acid profiles

Discrimination of Chinese rice wines according to ageing time and brand using amino acid profiles was presented in this study. Free amino acids (16) in 98 rice wines were simultaneously determined using high-performance liquid chromatograph-diode array detection (HPLC-DAD). Then the experimental data was subjected to multivariate analysis. Principal component analysis (PCA) was employed to differentiate samples from various ageing times (3, 9, 11 and 15months) and brands ("pagoda", "kuaijishan", and "guyuelongshan"). Partial least square discriminant analysis (PLS-DA) and full (leave-one-out) cross-validation were used to develop classification models. The overall correct classification rate for different ageing times and brands was 99.7% and 94.9%, respectively. The proposed method shows an effective strategy for the detection of mislabelling of rice wines.

Adductomics: characterizing exposures to reactive electrophiles

To understand environmental causes of disease, unbiased methods are needed to characterize the human exposome, which represents all toxicants to which people are exposed from both exogenous and endogenous sources. Because they directly modify DNA and important proteins, reactive electrophiles are probably the most important constituents of the exposome. Exposures to reactive electrophiles can be characterized by measuring adducts from reactions between circulating electrophiles and blood nucleophiles. We define an 'adductome' as the totality of such adducts with a given nucleophilic target. Because of their greater abundance and residence times in human blood, adducts of hemoglobin (Hb) and human serum albumin (HSA) are preferable to those of DNA and glutathione for characterizing adductomes. In fact, the nucleophilic hotspot represented by the only free sulfhydryl group in HSA (HSA-Cys(34)) offers particular advantages for adductomic experiments. Although targeted adducts of HSA-Cys(34) have been monitored for decades, an unbiased method has only recently been reported for visualizing the HSA-Cys(34) 'subadductome'. The method relies upon a novel mass spectrometry application, termed fixed-step selected reaction monitoring (FS-SRM), to profile Cys(34) adducts in tryptic digests of HSA. Here, we selectively review the literature regarding the potential of adductomics to partially elucidate the human exposome, with particular attention to the HSA-Cys(34) subadductome.

Analysis of polar metabolites by hydrophilic interaction chromatography–MS/MS

Increasing emphasis has been placed on quantitative characterization of drug metabolites during drug discovery and development. Due to the more polar nature of drug metabolites, quantitative analysis using traditional reversed-phase liquid chromatography tandem mass spectrometry (RPLC–MS/MS) can be quite challenging. As an alternative chromatographic mode, hydrophilic interaction chromatography (HILIC) offers unique advantages for analysis of polar metabolites, providing better retention/separation, higher sensitivity, higher efficiency and potential for ultra-fast analysis to improve throughput. In this article, selected case studies from the authors’ own laboratory, and examples from current literature, will be discussed to demonstrate some practical considerations for method development of HILIC–MS/MS assays. The effectiveness of using HILIC–MS/MS for mitigating analytical challenges associated with quantitation of polar metabolites, including phase I and II metabolites of drugs, as well as endogenous metabolites, will be exhibited.

Database searching for structural identification of metabolites in complex biofluids for mass spectrometry-based metabonomics

MS and HPLC are commonly used for compound characterization and obtaining structural information; in the field of metabonomics, these two analytical techniques are often combined to characterize unknown endogenous or exogenous metabolites present in complex biological samples. Since the structures of a majority of these metabolites are not actually identified, the result of most metabonomic studies is a list of m/z values and retention times. However, without knowing actual structures, the biological significance of these 'features' cannot be determined. The process of identifying the structures of unknown compounds can be time intensive, costly and frequently requires the use of multiple orthogonal analytical techniques - this laborious procedure seems insurmountable for the long lists of unknowns that must be identified for each study. In addition, the limited sample volume and the extremely low concentration of most endogenous analytes frequently make purification and identification by other instrumentation nearly impossible. This review is intended to explore the problems and progress with current tools that are available for MS-based structure identification for both endogenous and exogenous metabolites.

Profiling Cys34 adducts of human serum albumin by fixed-step selected reaction monitoring

A method is described for profiling putative adducts (or other unknown covalent modifications) at the Cys34 locus of human serum albumin (HSA), which represents the preferred reaction site for small electrophilic species in human serum. By comparing profiles of putative HSA-Cys34 adducts across populations of interest it is theoretically possible to explore environmental causes of degenerative diseases and cancer caused by both exogenous and endogenous chemicals. We report a novel application of selected-reaction-monitoring (SRM) mass spectrometry, termed fixed-step SRM (FS-SRM), that allows detection of essentially all HSA-Cys34 modifications over a specified range of mass increases (added masses). After tryptic digestion, HSA-Cys34 adducts are contained in the third largest peptide (T3), which contains 21 amino acids and an average mass of 2433.87 Da. The FS-SRM method does not require that exact masses of T3 adducts be known in advance but rather uses a theoretical list of T3-adduct m/z values separated by a fixed increment of 1.5. In terms of added masses, each triply charged parent ion represents a bin of ±2.3 Da between 9.1 Da and 351.1 Da. Synthetic T3 adducts were used to optimize FS-SRM and to establish screening rules based upon selected b- and y-series fragment ions. An isotopically labeled T3 adduct is added to protein digests to facilitate quantification of putative adducts. We used FS-SRM to generate putative adduct profiles from six archived specimens of HSA that had been pooled by gender, race, and smoking status. An average of 66 putative adduct hits (out of a possible 77) were detected in these samples. Putative adducts covered a wide range of concentrations, were most abundant in the mass range below 100 Da, and were more abundant in smokers than in nonsmokers. With minor modifications, the FS-SRM methodology can be applied to other nucleophilic sites and proteins.

Metabolic Profiling of Human Blood by High Resolution Ion Mobility Mass Spectrometry (IM-MS)

A high resolution ion mobility time-of-flight mass spectrometer with electrospray ionization source (ESI-IM-MS) was evaluated as an analytical method for rapid analysis of complex biological samples such as human blood metabolome was investigated. The hybrid instrument (IM-MS) provided an average ion mobility resolving power of ~90 and a mass resolution of ~1500 (at m/z 100). A few µL of whole blood was extracted with methanol, centrifuged and infused into the IM-MS via an electrospray ionization source. Upon IM-MS profiling of the human blood metabolome approximately 1,100 metabolite ions were detected and 300 isomeric metabolites separated in short analyses time (30 minutes). Estimated concentration of the metabolites ranged from the low micromolar to the low nanomolar level. Various classes of metabolites (amino acids, organic acids, fatty acids, carbohydrates, purines and pyrimidines etc) were found to form characteristic mobility-mass correlation curves (MMCC) that aided in metabolite identification. Peaks corresponding to various sterol derivatives, estrogen derivatives, phosphocholines, prostaglandins, and cholesterol derivatives detected in the blood extract were found to occupy characteristic two dimensional IM-MS space. Low abundance metabolite peaks that can be lost in MS random noise were resolved from noise peaks by differentiation in mobility space. In addition, the peak capacity of MS increased six fold by coupling IMS prior to MS analysis.

Metabolic profiling of Escherichia coli by ion mobility-mass spectrometry with MALDI ion source

Comprehensive metabolome analysis using mass spectrometry (MS) often results in a complex mass spectrum and difficult data analysis resulting from the signals of numerous small molecules in the metabolome. In addition, MS alone has difficulty measuring isobars and chiral, conformational and structural isomers. When a matrix-assisted laser desorption ionization (MALDI) source is added, the difficulty and complexity are further increased. Signal interference between analyte signals and matrix ion signals produced by MALDI in the low mass region (<1500 Da) cause detection and/or identification of metabolites difficult by MS alone. However, ion mobility spectrometry (IMS) coupled with MS (IM-MS) provides a rapid analytical tool for measuring subtle structural differences in chemicals. IMS separates gas-phase ions based on their size-to-charge ratio. This study, for the first time, reports the application of MALDI to the measurement of small molecules in a biological matrix by ion mobility-time of flight mass spectrometry (IM-TOFMS) and demonstrates the advantage of ion-signal dispersion in the second dimension. Qualitative comparisons between metabolic profiling of the Escherichia coli metabolome by MALDI-TOFMS, MALDI-IM-TOFMS and electrospray ionization (ESI)-IM-TOFMS are reported. Results demonstrate that mobility separation prior to mass analysis increases peak-capacity through added dimensionality in measurement. Mobility separation also allows detection of metabolites in the matrix-ion dominated low-mass range (m/z < 1500 Da) by separating matrix signals from non-matrix signals in mobility space.

Targeted serum metabolite profiling of nucleosides in esophageal adenocarcinoma

Nucleosides are indicators of the whole-body turnover of transfer RNA. Based on the activity of cancer cells these molecules could potentially be used as cancer biomarkers, and several studies have determined that the metabolic levels of nucleosides are significantly altered in cancer patients compared to control groups. Here we report a targeted metabolite investigation of serum nucleosides in esophageal adenocarcinoma specimens. We quantified eight nucleosides using high-performance liquid chromatography/triple quadrupole mass spectrometry (HPLC/TQMS) and determined that the metabolic levels of 1-methyladenosine (p <2.14 × 10(-7)), N(2),N(2)-dimethylguanosine (p <2.78 × 10(-7)), N(2)-methylguanosine (p <2.48 × 10(-6)) and cytidine (p <6.98 × 10(-4)) were significantly elevated while the concentration of uridine (p <3.74 × 10(-3)) was significantly lowered in serum samples from cancer patients compared to those of control group. Our results suggest that nucleosides could potentially serve as useful biomarkers to identify esophageal adenocarcinoma.

Toward an "omic" physiopathology of reactive chemicals: thirty years of mass spectrometric study of the protein adducts with endogenous and xenobiotic compounds

Cancer and degenerative diseases are major causes of morbidity and death, derived from the permanent modification of key biopolymers such as DNA and regulatory proteins by usually smaller, reactive molecules, present in the environment or generated from endogenous and xenobiotic components by the body's own biochemical mechanisms (molecular adducts). In particular, protein adducts with organic electrophiles have been studied for more than 30 [see, e.g., Calleman et al., 1978] years essentially for three purposes: (a) as passive monitors of the mean level of individual exposure to specific chemicals, either endogenously present in the human body or to which the subject is exposed through food or environmental contamination; (b) as quantitative indicators of the mean extent of the individual metabolic processing which converts a non-reactive chemical substance into its toxic products able to damage DNA (en route to cancer induction through genotoxic mechanisms) or key proteins (as in the case of several drugs, pesticides or otherwise biologically active substances); (c) to relate the extent of protein modification to that of biological function impairment (such as enzyme inhibition) finally causing the specific health damage. This review describes the role that contemporary mass spectrometry-based approaches employed in the qualitative and quantitative study of protein-electrophile adducts play in the discovery of the (bio)chemical mechanisms of toxic substances and highlights the future directions of research in this field. A particular emphasis is given to the measurement of often high levels of the protein adducts of several industrial and environmental pollutants in unexposed human populations, a phenomenon which highlights the possibility that a number of small organic molecules are generated in the human organism through minor metabolic processes, the imbalance of which may be the cause of "spontaneous" cases of cancer and of other degenerative diseases of still uncharacterized etiology. With all this in mind, it is foreseen that a holistic description of cellular functions will take advantage of new analytical methods based on time-integrated metabolomic measurements of a new biological compartment, the "adductome," aimed at better understanding integrated organism response to environmental and endogenous stressors.

RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer

A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.

Mass defect filter technique and its applications to drug metabolite identification by high-resolution mass spectrometry

Identification of drug metabolites by liquid chromatography/mass spectrometry (LC/MS) involves metabolite detection in biological matrixes and structural characterization based on product ion spectra. Traditionally, metabolite detection is accomplished primarily on the basis of predicted molecular masses or fragmentation patterns of metabolites using triple-quadrupole and ion trap mass spectrometers. Recently, a novel mass defect filter (MDF) technique has been developed, which enables high-resolution mass spectrometers to be utilized for detecting both predicted and unexpected drug metabolites based on narrow, well-defined mass defect ranges for these metabolites. This is a new approach that is completely different from, but complementary to, traditional molecular mass- or MS/MS fragmentation-based LC/MS approaches. This article reviews the mass defect patterns of various classes of drug metabolites and the basic principles of the MDF approach. Examples are given on the applications of the MDF technique to the detection of stable and chemically reactive metabolites in vitro and in vivo. Advantages, limitations, and future applications are also discussed on MDF and its combinations with other data mining techniques for the detection and identification of drug metabolites.

Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations

In this study, urinary metabolites from liver cancer patients and healthy volunteers were studied by a metabonomic method based on ultra performance liquid chromatography coupled to mass spectrometry. Both hydrophilic interaction chromatography (HILIC) and reversed-phase liquid chromatography (RPLC) were used to separate the urinary metabolites. Principle component analysis (PCA) and partial least squares to latent structure-discriminant analysis (PLS-DA) models were built to separate the healthy volunteers from the liver cancer patients and to find compounds that are expressed in significantly different amounts between the two populations. 21 metabolite ions were considered as potential biomarkers according to the Variable importance in the Project (VIP) value and S-plot. Compared with RPLC, a more sensitive and stable response can be recorded in HILIC mode due to the high content of organic solvent used. Moreover, the liver cancer group and the healthy volunteers can be better separated based on the data from the HILIC separation, which indicates that HILIC is suitable for urinary metabonomic analysis. In HILIC mode, several polar compounds related to arginine and proline metabolism, alanine and aspartate metabolism, lysine degradation, nicotinate and nicotinamide metabolism were found to be significantly changed in the concentrations of the two different populations: healthy and cancer. In contrast, in RPLC mode, these changed compounds are related to fatty acids oxidation.

Advances in separation science applied to metabonomics

Metabonomics focuses on metabolite profile changes in diverse living systems caused by a biological perturbation. These metabolite signatures can be achieved with techniques such as gas chromatography, high-performance liquid chromatography (ultra-high-performance/pressure liquid chromatography and capHPLC), capillary electrophoresis, and capillary electrochromatography normally hyphenated with MS. In this review we present the latest developments of the abovementioned techniques applied in the field of metabonomics, with applications covering phytochemistry, toxicology and clinical research.

Rapid screening and characterization of drug metabolites using a multiple ion monitoring-dependent MS/MS acquisition method on a hybrid triple quadrupole-linear ion trap mass spectrometer

A novel LC/MS/MS method that uses multiple ion monitoring (MIM) as a survey scan to trigger the acquisition of enhanced product ions (EPI) on a hybrid quadrupole-linear ion trap mass spectrometer (Q TRAP) was developed for drug metabolite identification. In the MIM experiment, multiple predicted metabolite ions were monitored in both Q1 and Q3. The collision energy in Q2 was set to a low value to minimize fragmentation. Results from analyzing ritonavir metabolites in rat hepatocytes demonstrate that MIM-EPI was capable of targeting a larger number of metabolites regardless of their fragmentation and retained sensitivity and duty cycle similar to multiple reaction monitoring (MRM)-EPI. MIM-based scanning methods were shown to be particularly useful in several applications. First, MIM-EPI enabled the sensitive detection and MS/MS acquisition of up to 100 predicted metabolites. Second, MIM-MRM-EPI was better than MRM-EPI in the analysis of metabolites that undergo either predictable or unpredictable fragmentation pathways. Finally, a combination of MIM-EPI and full-scan MS (EMS), as an alternative to EMS-EPI, was well suited for routine in vitro metabolite profiling. Overall, MIM-EPI significantly enhanced the metabolite identification capability of the hybrid triple quadrupole-linear ion trap LC/MS.

A metabonomic characterization of CCl4-induced acute liver failure using partial least square regression based on the GC/MS metabolic profiles of plasma in mice

This work characterized the metabolism disorders of acute liver failure (ALF) induced by carbon tetrachloride (CCl(4)) in a mouse model with different dosage of intoxication (100, 500 and 1000 mg/kg). Metabolic profiles of mice plasma were detected by gas chromatography/mass spectrometry (GC/MS) after chemical derivatization. Here an effective information-extracting approach was implemented on the basis of partial least square regression analysis (PLS-RA). PLS modeling was achieved with two kinds of Y-vectors for the acquired metabonomics data and eight metabolites with different changing behaviors were selected. ALF of mice induced by CCl(4) was characterized by the elevation of glutamate, citrate, serine and threonine, as well as the decrease of alpha-glycerophosphate, docosahexaenoic acid, palmitic acid and oleic acid in plasma. The difference in the concentrations of serine, threonine, palmitic acid and oleic acid remained insignificant between the control and 100mg/kg groups, while significant distinction appeared when comparing the control and two higher dosed groups. The underlying regulation of CCl(4)-perturbed metabolic pathways was discussed according to the selected metabolites. The present study demonstrated a great potential of PLS-RA in exploiting a comprehensive metabolic effects of CCl(4) intoxication and its efficient capability to reveal the hepatotoxic mechanism of ALF induced by reactive oxygen species (ROS).

Global metabolic profiling analysis on human urine by UPLC-TOFMS: issues and method validation in nutritional metabolomics

Optimisation and method validation was assessed here for metabolic profiling analysis of urine samples using UPLC-TOFMS. A longer run time of 31 min revealed greater reproducibility, and the higher number of variables was identified as compared to shortened run times (10 and 26 min). We have also implemented two QC urine samples enabling the assessment of the quality and reproducibility of the data generated during the whole analytical workflow (retention time drift, mass precision and fluctuation of the ion responses over time). Based on the QC data, suitable standards for ensuring consistent analytical results for metabolomics applications using the UPLC-MS techniques are recommended.

Metabonomics evaluation of urine from rats given acute and chronic doses of acetaminophen using NMR and UPLC/MS

Urinary metabolic perturbations associated with acute and chronic acetaminophen-induced hepatotoxicity were investigated using nuclear magnetic resonance (NMR) spectroscopy and ultra performance liquid chromatography/mass spectrometry (UPLC/MS) metabonomics approaches to determine biomarkers of hepatotoxicity. Acute and chronic doses of acetaminophen (APAP) were administered to male Sprague-Dawley rats. NMR and UPLC/MS were able to detect both drug metabolites and endogenous metabolites simultaneously. The principal component analysis (PCA) of NMR or UPLC/MS spectra showed that metabolic changes observed in both acute and chronic dosing of acetaminophen were similar. Histopathology and clinical chemistry studies were performed and correlated well with the PCA analysis and magnitude of metabolite changes. Depletion of antioxidants (e.g. ferulic acid), trigonelline, S-adenosyl-L-methionine, and energy-related metabolites indicated that oxidative stress was caused by acute and chronic acetaminophen administration. Similar patterns of metabolic changes in response to acute or chronic dosing suggest similar detoxification and recovery mechanisms following APAP administration.

Liquid chromatography/mass spectrometry for metabonomics investigation of the biochemical effects induced by aristolochic acid in rats: the use of information-dependent acquisition for biomarker identification

The toxic effects of oral administrations of nephrotoxic and carcinogenic aristolochic acid (AA) to male Sprague-Dawley rats were investigated by using high-performance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometer. Analysis of the urine and plasma samples revealed distinct changes in the biochemical patterns in the AA-dosed rats. After peak finding and alignment, principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were used for multivariate data analysis. Potential biomarkers were studied by high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) analyses. The MS/MS spectra of all endogenous metabolites satisfying the pre-defined criteria were acquired in a single information-dependent acquisition (IDA) analysis, demonstrating that IDA was an efficient approach for structural elucidation in metabonomic studies. Citric acid and a glucuronide-containing metabolite were observed as potential biomarkers in rat urine. A significant increase in plasma creatinine concentration was also observed in the AA-dosed rats, which indicated that AA induced an adverse effect on the renal clearance function.

LC-MS-based metabonomics analysis

Metabonomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. It has shown particular promise in the areas of toxicology and drug development, functional genomics, systems biology, and clinical diagnosis. Comprehensive metabonomics investigations are primarily a challenge for analytical chemistry. High-performance liquid chromatography-mass spectrometry (HPLC-MS) is an established technology in drug metabolite analysis and is now expanding into endogenous metabolite research. Its main advantages include wide dynamic range, reproducible quantitative analysis, and the ability to analyze biofluids with extreme molecular complexity. The aims of developing HPLC-MS for metabonomics range from understanding basic biochemistry to biomarker discovery and the structural characterization of physiologically important metabolites. In this review, the strategy and application of HPLC-MS-based metabonomics are reviewed.

Class selection of amino acid metabolites in body fluids using chemical derivatization and their enhanced 13C NMR

We report a chemical derivatization method that selects a class of metabolites from a complex mixture and enhances their detection by 13C NMR. Acetylation of amines directly in aqueous medium with 1,1'-13C(2) acetic anhydride is a simple method that creates a high sensitivity and quantitative label in complex biofluids with minimal sample pretreatment. Detection using either 1D or 2D 13C NMR experiments produces highly resolved spectra with improved sensitivity. Experiments to identify and compare amino acids and related metabolites in normal human urine and serum samples as well as in urine from patients with the inborn errors of metabolism tyrosinemia type II, argininosuccinic aciduria, homocystinuria, and phenylketonuria demonstrate the method. The use of metabolite derivatization and 13C NMR spectroscopy produces data suitable for metabolite profiling analysis of biofluids on a time scale that allows routine use. Extension of this approach to enhance the NMR detection of other classes of metabolites has also been accomplished. The improved detection of low-concentration metabolites shown here creates opportunities to improve the understanding of the biological processes and develop improved disease detection methodologies.

Analyses of representative biomarkers of exposure and effect by chromatographic, mass spectrometric, and nuclear magnetic resonance techniques: method development and application in life sciences

Biomarkers are essential tools in monitoring studies, which include environmental monitoring, biological monitoring, biological effect monitoring, and health surveillance, as well as drug development processes. Their discovery, validation, and analysis require highly sensitive and selective analytical technologies. In this regard, gas and liquid chromatography, mass spectrometry, and nuclear magnetic resonance spectroscopy have facilitated great achievements in all these areas. In addition and closely related to biomarkers, the ongoing developments in these techniques promise a better understanding of the nature and mechanisms of toxic effects originating from various chemical, biological, or physical sources. This Review compiles studies performed on selected biomarkers with respect to both method development and application. Section 1 summarizes the concept of biomarkers; their application in various industrial/occupational, agricultural, drug developmental, and medical/clinical platforms. This section also focuses on biotransformation studies in close relation to biomarker discovery and validation, and on major techniques utilized in this area. In Section 2, biotransformation of volatile anesthetics in humans with a focus on mercapturic acid derivatives as potential biomarkers of effect is reviewed. The use of GC-ECD, GC/MS, and 19F-NMR in these studies is described. Section 3 focuses on the analysis of aldehydic lipid peroxidation degradation products by GC-ECD in mammalian cells in which oxidative stress induced chemically, and in humans after various challenges; anesthetic exposure, ischemia-reperfusion, and controlled endurance exercise. In Section 4, method development for protein and DNA oxidation products by LC-tandem MS and its application in mammalian cells and in humans are summarized. Possibilities, limitations, and future perspectives are discussed in Section 5.

Chemometrics in metabonomics

We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data. This includes the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS (OPLS), and dynamic extensions thereof. This is illustrated by examples from the literature.

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.

Tools in Metabonomics: An Integrated Validation Approach for LC-MS Metabolic Profiling of Mercapturic Acids in Human Urine

While for 1H NMR techniques there already exist common analytical and reporting standards, this does not apply to LC-MS metabolic profiling approaches. These standards are the more recommended when applying metabonomics to human biofluids, particularly urine samples, due to the high degree of biological variation compared to animals. A control study was performed, and urine samples of 30 healthy male and female human subjects were collected at intervals of 8 h twice a day for three consecutive days. Using selective multiple reaction monitoring in combination with a column-switching tool for the analysis of the mercapturate pattern, samples were screened for time and gender differences, the most common confounders. Data preprocessing parameters, alignment, scaling to internal standards, and normalization techniques were optimized by PCA, PLS-DA, and OPLS models. Great care was taken in the validation process of both analytical and chemometric protocols. Additionally, a problem of LC-MS, the combination of "different-batch" data to "one-batch" data could be solved by a batchwise scaling procedure. Based on these results, the use of metabolic profiling via mercapturates will be feasible for the detection of disease or toxicity markers in the future since mercapturates are important biomarkers of reactive metabolites known to be involved in many toxic processes.

Monitoring Diet Effects via Biofluids and Their Implications for Metabolomics Studies

The effect of diet on metabolites found in rat urine samples has been investigated using nuclear magnetic resonance (NMR) and a new ambient ionization mass spectrometry experiment, extractive electrospray ionization mass spectrometry (EESI-MS). Urine samples from rats with three different dietary regimens were readily distinguished using multivariate statistical analysis on metabolites detected by NMR and MS. To observe the effect of diet on metabolic pathways, metabolites related to specific pathways were also investigated using multivariate statistical analysis. Discrimination is increased by making observations on restricted compound sets. Changes in diet at 24-h intervals led to predictable changes in the spectral data. Principal component analysis was used to separate the rats into groups according to their different dietary regimens using the full NMR, EESI-MS data or restricted sets of peaks in the mass spectra corresponding only to metabolites found in the urea cycle and metabolism of amino groups pathway. By contrast, multivariate analysis of variance from the score plots showed that metabolites of purine metabolism obscure the classification relative to the full metabolite set. These results suggest that it may be possible to reduce the number of statistical variables used by monitoring the biochemical variability of particular pathways. It should also be possible by this procedure to reduce the effect of diet in the biofluid samples for such purposes as disease detection.

Investigation of analytical variation in metabonomic analysis using liquid chromatography/mass spectrometry

Sources of analytical variation in high-performance liquid chromatography/mass spectrometry (HPLC/MS), such as changes in retention, mass accuracy or signal intensity, have been investigated to assess their importance as a variable in the metabonomic analysis of human urine. In this study chromatographic retention and mass accuracy were found to be quite reproducible with the most significant source of analytical variation in the data sets obtained being the result of changes in detector response. Depending on the signal intensity threshold used to define the presence of a peak a sample component could be present in some replicate injections and absent in others within the same run. The implementation of a more sophisticated data software analysis package was found to greatly reduce the impact of detector response variability resulting in improved data analysis.

Ultra-performance liquid chromatography/time-of-flight mass spectrometry based metabolomics of raw and steamed Panax notoginseng

At present, metabolite profiling is of growing importance in herbal medicine fields such as breeding, formulation, quality control and clinical trials. This preliminary study indicated that ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC/TOFMS)-based metabolomics allows direct detection of down-stream derivatives of metabolites, arising from the herbal formulation process. This analytical approach allows the discrimination and tentative authentication of unique biomarkers related to different herbal extracts using unsupervised multivariate principal component analysis (PCA). The tentative identification of biomarkers is complemented significantly by the accurate mass measurement of TOFMS and the high resolution and high retention time reproducibility rendered by UPLC. The application of this approach in herbal extract discrimination and ginsenoside biomarker discovery of raw and steamed Panax notoginseng (Burk.) F.H. Chen is demonstrated and discussed.