Evidence of different metabolic phenotypes in humans. Proc Natl Acad Sci U S A. 2008;105:1420-1424. [PMID: 18230739 DOI: 10.1073/pnas.0705685105]

Development and validation of a standardized protocol to monitor human dietary exposure by metabolite fingerprinting of urine samples

Conventional tools for measuring dietary exposure have well recognized limitations. Measurement of food-derived metabolites in biofluids provides an alternative approach and our aim was to develop an experimental protocol which ensures that extraneous variability does not obscure metabolic signals from ingested foods. Healthy adults consumed a standardized meal in the evening before each test day and collected pooled overnight urine. On each test day of three different studies, urine was collected in the fasted state and at different time points after consumption of a standardized breakfast. Metabolite fingerprinting of samples using Flow Infusion Electrospray-Ionization Mass Spectrometry followed by multivariate data analysis showed strong discrimination between overnight, fasting and postprandial samples, in each study separately and when data from the three studies were pooled. Such differences were robust and highly reproducible within individuals on separate occasions. Urine volume was an efficient data normalization factor for metabolite fingerprinting data. Postprandial urines had a stable chemical composition over a period of 2-4 h after eating a standardized breakfast, suggesting that there is a flexible time window for urine collection. Fasting urine samples provided a stable baseline for universal comparisons with postprandial samples. A dietary exposure biomarker discovery protocol was validated by demonstrating that top-ranked signals discriminating between fasting and 2-4 h postprandial urine samples could be linked to metabolites abundant in some components of the standardized breakfast. We conclude that the protocol developed will have value in the search for biomarker leads of dietary exposure. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0289-0) contains supplementary material, which is available to authorized users.

Urine metabolic signature of pancreatic ductal adenocarcinoma by (1)h nuclear magnetic resonance: identification, mapping, and evolution

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis and is highly chemoresistant. Early detection is the only means to impact long-term survival, but screening methods are lacking. Given the complex and heterogeneous nature of pancreatic cancer, unbiased analytical methods such as metabolomics by nuclear magnetic resonance (NMR) spectroscopy show promise to identify disease-specific molecular fingerprints. NMR profiles constitute a fingerprint of the biofluid, reporting quantitatively on all detectable small biomolecules. NMR spectroscopy was applied to investigate the urine metabolome of PDAC patients (n = 33) and to detect altered metabolic profiles in comparison with healthy matched controls (n = 54). The spectral data were analyzed using multivariate statistical techniques. Statistically significant differences were found between urine metabolomic profiles of PDAC and control individuals (p < 10(-5)). Group discrimination was possible due to average concentration differences of several metabolite signals, pointing to a multimolecular signature of the disease. The robustness of the determined statistical model is confirmed by its predictive performance (sensitivity = 75.8%, specificity = 90.7%). Additionally, the method allowed for a neat separation between spectral profiles of individuals with intermediate and advanced pathologic staging, as well as for the discrimination of samples based on tumor localization. NMR spectroscopy analysis of urinary metabolic profiles proved successful in identifying a complex molecular signature of PDAC. Furthermore, results of a descriptive-level analysis show the possibility to follow disease evolution and to carry out tumor site mapping. Given the high reproducibility and the noninvasive nature of the analytical procedure, the described method bears potential to impact large-scale screening programs.

Metabolomic NMR fingerprinting to identify and predict survival of patients with metastatic colorectal cancer

Earlier detection of patients with metastatic colorectal cancer (mCRC) might improve their treatment and survival outcomes. In this study, we used proton nuclear magnetic resonance ((1)H-NMR) to profile the serum metabolome in patients with mCRC and determine whether a disease signature may exist that is strong enough to predict overall survival (OS). In 153 patients with mCRC and 139 healthy subjects from three Danish hospitals, we profiled two independent sets of serum samples in a prospective phase II study. In the training set, (1)H-NMR metabolomic profiling could discriminate patients with mCRC from healthy subjects with a cross-validated accuracy of 100%. In the validation set, 96.7% of subjects were correctly classified. Patients from the training set with maximally divergent OS were chosen to construct an OS predictor. After validation, patients predicted to have short OS had significantly reduced survival (HR, 3.4; 95% confidence interval, 2.06-5.50; P = 1.33 × 10(-6)). A number of metabolites concurred with the (1)H-NMR fingerprint of mCRC, offering insights into mCRC metabolic pathways. Our findings establish that (1)H-NMR profiling of patient serum can provide a strong metabolomic signature of mCRC and that analysis of this signature may offer an independent tool to predict OS.

Use of mass spectrometry fingerprinting to identify urinary metabolites after consumption of specific foods

BACKGROUND

The lack of robust biological markers of dietary exposure hinders the quantitative understanding of causal relations between diet and health.

OBJECTIVE

We aimed to develop an efficient procedure to discover metabolites in urine that may have future potential as biomarkers of acute exposure to foods of high public health importance.

DESIGN

Twenty-four participants were provided with a test breakfast in which the cereal component of a standardized breakfast was replaced by 1 of 4 foods of high public health importance; 1.5-, 3-, and 4.5-h postprandial urine samples were collected. Flow infusion electrospray-ionization mass spectrometry followed by supervised multivariate data analysis was used to discover signals resulting from consumption of each test food.

RESULTS

Fasted-state urine samples provided a universal comparator for food biomarker lead discovery in postprandial urine. The filtering of data features associated with consumption of the common components of the standardized breakfast improved discrimination models and readily identified metabolites that showed consumption of specific test foods. A combination of trimethylamine-N-oxide and 1-methylhistidine was associated with salmon consumption. Novel ascorbate derivatives were discovered in urine after consumption of either broccoli or raspberries. Sulphonated caffeic acid and sulphonated methyl-epicatechin concentrations increased dramatically after consumption of raspberries.

CONCLUSIONS

This biomarker lead discovery strategy can identify urinary metabolites associated with acute exposure to individual foods. Future studies are required to validate the specificity and utility of potential biomarkers in an epidemiologic context.

Cross-platform analysis of longitudinal data in metabolomics

Metabolic profiling is considered to be a very promising tool for diagnostic purposes, for assessing nutritional status and response to drugs. However, it is also evident that human metabolic profiles have a complex nature, influenced by many external factors. This, together with the understanding of the difficulty to assign people to distinct groups and a general move in clinical science towards personalized medicine, raises the interest to explore individual and variable metabolic features for each individual separately in longitudinal study design. In the current paper we have analyzed a set of metabolic profiles of a selection of six urine samples per person from a set of healthy individuals by (1)H NMR and reversed-phase UPLC-MS. We have demonstrated that the method for recovery of individual metabolic phenotypes can give complementary information to another established method for analysis of longitudinal data--multilevel component analysis. We also show that individual metabolic signatures can be found not only in (1)H NMR data, as has been demonstrated before, but also even more strongly in LC-MS data.

Human metabolic profiles are stably controlled by genetic and environmental variation

A comprehensive variation map of the human metabolome identifies genetic and stable-environmental sources as major drivers of metabolite concentrations. The data suggest that sample sizes of a few thousand are sufficient to detect metabolite biomarkers predictive of disease.

We designed a longitudinal twin study to characterize the genetic, stable-environmental, and longitudinally fluctuating influences on metabolite concentrations in two human biofluids—urine and plasma—focusing specifically on the representative subset of metabolites detectable by ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

We identified widespread genetic and stable-environmental influences on the (urine and plasma) metabolomes, with (30 and 42%) attributable on average to familial sources, and (47 and 60%) attributable to longitudinally stable sources.

Ten of the metabolites annotated in the study are estimated to have >60% familial contribution to their variation in concentration.

Our findings have implications for the design and interpretation of ¹H NMR-based molecular epidemiology studies. On the basis of the stable component of variation quantified in the current paper, we specified a model of disease association under which we inferred that sample sizes of a few thousand should be sufficient to detect disease-predictive metabolite biomarkers.

Metabolites are small molecules involved in biochemical processes in living systems. Their concentration in biofluids, such as urine and plasma, can offer insights into the functional status of biological pathways within an organism, and reflect input from multiple levels of biological organization—genetic, epigenetic, transcriptomic, and proteomic—as well as from environmental and lifestyle factors. Metabolite levels have the potential to indicate a broad variety of deviations from the ‘normal' physiological state, such as those that accompany a disease, or an increased susceptibility to disease. A number of recent studies have demonstrated that metabolite concentrations can be used to diagnose disease states accurately. A more ambitious goal is to identify metabolite biomarkers that are predictive of future disease onset, providing the possibility of intervention in susceptible individuals.

If an extreme concentration of a metabolite is to serve as an indicator of disease status, it is usually important to know the distribution of metabolite levels among healthy individuals. It is also useful to characterize the sources of that observed variation in the healthy population. A proportion of that variation—the heritable component—is attributable to genetic differences between individuals, potentially at many genetic loci. An effective, molecular indicator of a heritable, complex disease is likely to have a substantive heritable component. Non-heritable biological variation in metabolite concentrations can arise from a variety of environmental influences, such as dietary intake, lifestyle choices, general physical condition, composition of gut microflora, and use of medication. Variation across a population in stable-environmental influences leads to long-term differences between individuals in their baseline metabolite levels. Dynamic environmental pressures lead to short-term fluctuations within an individual about their baseline level. A metabolite whose concentration changes substantially in response to short-term pressures is relatively unlikely to offer long-term prediction of disease. In summary, the potential suitability of a metabolite to predict disease is reflected by the relative contributions of heritable and stable/unstable-environmental factors to its variation in concentration across the healthy population.

Studies involving twins are an established technique for quantifying the heritable component of phenotypes in human populations. Monozygotic (MZ) twins share the same DNA genome-wide, while dizygotic (DZ) twins share approximately half their inherited DNA, as do ordinary siblings. By comparing the average extent of phenotypic concordance within MZ pairs to that within DZ pairs, it is possible to quantify the heritability of a trait, and also to quantify the familiality, which refers to the combination of heritable and common-environmental effects (i.e., environmental influences shared by twins in a pair). In addition to incorporating twins into the study design, it is useful to quantify the phenotype in some individuals at multiple time points. The longitudinal aspect of such a study allows environmental effects to be decomposed into those that affect the phenotype over the short term and those that exert stable influence.

For the current study, urine and blood samples were collected from a cohort of MZ and DZ twins, with some twins donating samples on two occasions several months apart. Samples were analysed by ¹H nuclear magnetic resonance (¹H NMR) spectroscopy—an untargeted, discovery-driven technique for quantifying metabolite concentrations in biological samples. The application of ¹H NMR to a biological sample creates a spectrum, made up of multiple peaks, with each peak's size quantitatively representing the concentration of its corresponding hydrogen-containing metabolite.

In each biological sample in our study, we extracted a full set of peaks, and thereby quantified the concentrations of all common plasma and urine metabolites detectable by ¹H NMR. We developed bespoke statistical methods to decompose the observed concentration variation at each metabolite peak into that originating from familial, individual-environmental, and unstable-environmental sources.

We quantified the variability landscape across all common metabolite peaks in the urine and plasma ¹H NMR metabolomes. We annotated a subset of peaks with a total of 65 metabolites; the variance decompositions for these are shown in Figure 1. Ten metabolites' concentrations were estimated to have familial contributions in excess of 60%. The average proportion of stable variation across all extracted metabolite peaks was estimated to be 47% in the urine samples and 60% in the plasma samples; the average estimated familiality was 30% for urine and 42% for plasma. These results comprise the first quantitative variation map of the ¹H NMR metabolome. The identification and quantification of substantive widespread stability provides support for the use of these biofluids in molecular epidemiology studies. On the basis of our findings, we performed power calculations for a hypothetical study searching for predictive disease biomarkers among ¹H NMR-detectable urine and plasma metabolites. Our calculations suggest that sample sizes of 2000–5000 should allow reliable identification of disease-predictive metabolite concentrations explaining 5–10% of disease risk, while greater sample sizes of 5000–20 000 would be required to identify metabolite concentrations explaining 1–2% of disease risk.

¹H Nuclear Magnetic Resonance spectroscopy (¹H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired ¹H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in ¹H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect ¹H NMR-based biomarkers quantifying predisposition to disease.

Simplivariate models: uncovering the underlying biology in functional genomics data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.

We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.

Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks

(1)H NMR metabolic profiling of urine, serum and plasma has been used to monitor the impact of the pre-analytical steps on the sample quality and stability in order to propose standard operating procedures (SOPs) for deposition in biobanks. We analyzed the quality of serum and plasma samples as a function of the elapsed time (t = 0-4 h) between blood collection and processing and of the time from processing to freezing (up to 24 h). The stability of the urine metabolic profile over time (up to 24 h) at various storage temperatures was monitored as a function of the different pre-analytical treatments like pre-storage centrifugation, filtration, and addition of the bacteriostatic preservative sodium azide. Appreciable changes in the profiles, reflecting changes in the concentration of a number of metabolites, were detected and discussed in terms of chemical and enzymatic reactions for both blood and urine samples. Appropriate procedures for blood derivatives collection and urine preservation/storage that allow maintaining as much as possible the original metabolic profile of the fresh samples emerge, and are proposed as SOPs for biobanking.

Secondary electrospray ionization-mass spectrometry: breath study on a control group

A series of fatty acids among other compounds have recently been detected in breath in real time by secondary electrospray ionization mass spectrometry (SESI-MS) (Martínez-Lozano P and Fernández de la Mora J 2008 Anal. Chem. 80 8210). Our main aim in this work was to (1) quantify their abundance in breath calibrating the system with standard vapors and (2) extend the study to a control group for several days, both under fasting conditions and after sucrose intake. For the quantitative study, we fed our system with controlled amounts (∼140-1440 ppt) of fatty acid vapors (i.e. propanoic, butanoic, pentanoic and hexanoic acids). As a result, we found sensitivities ranging between 1 and 2.2 cps/ppt. Estimated concentrations of these particular acids in the breath of a fasting subject were in the order of 100 ppt. These values were in reasonable agreement with those expected from reported typical plasma concentrations and Henry constants. A second set of experiments on three fasting individuals before and after ingesting 15 g of sucrose showed that the concentration of propionic and butanoic acids increased rapidly in breath for two subjects. This response was attributed to bacterial activity in mouth and pharynx. In contrast, a third subject showed no response to the administration of sucrose. In addition, we performed a survey among six fasting subjects comparing nasal and mouth exhalations during 11 days, 4 months apart. The signal intensity was comparable for mouth and nose breath. This observation, in conjunction with the quantitative study, suggests that these compounds are mostly systemic when measured under fasting conditions. We finally used the NIST MS search algorithm to evaluate the possibility of recognizing a breathing subject based on his/her breath signature. The global recognition score was 63% (41 out of 65), while the probability by chance alone was 6 × 10(-17). This indicates that (i) there are statistically recognizable differences in individual breath patterns and (ii) the breath pattern for a given subject is relatively stable in time. This is consistent with previous NMR-based studies indicating the existence of stable individual metabolic phenotypes.

Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting

BACKGROUND

Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.

METHODOLOGY/PRINCIPAL FINDINGS

40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid).

CONCLUSIONS/SIGNIFICANCE

Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.

Tocopherol transfer protein sensitizes prostate cancer cells to vitamin E

Prostate cancer is a major cause of mortality in men in developed countries. It has been reported that the naturally occurring antioxidant α-tocopherol (vitamin E) attenuates prostate cancer cell proliferation in cultured cells and mouse models. We hypothesized that overexpression of the tocopherol transfer protein (TTP), a vitamin E-binding protein that regulates tocopherol status, will sensitize prostate cancer cells to the anti-proliferative actions of the vitamin. To test this notion, we manipulated the expression levels of TTP in cultured prostate cells (LNCaP, PC3, DU145, and RWPE-1) using overexpression and knockdown approaches. Treatment of cells with tocopherol caused a time- and dose-dependent inhibition of cell proliferation. Overexpression of TTP dramatically sensitized the cells to the apoptotic effects of α-tocopherol, whereas reduction ("knockdown") of TTP expression resulted in resistance to the vitamin. TTP levels also augmented the inhibitory effects of vitamin E on proliferation in semi-solid medium. The sensitizing effects of TTP were paralleled by changes in the intracellular accumulation of a fluorescent analog of vitamin E and by a reduction in intracellular levels of reactive oxygen species and were not observed when a naturally occurring, ligand binding-defective mutant of TTP was used. We conclude that TTP sensitizes prostate cancer cells to the anti-proliferative effects of vitamin E and that this activity stems from the ability of protein to increase the intracellular accumulation of the antioxidant. These observations support the notion that individual changes in the expression level or activity of TTP may determine the responsiveness of prostate cancer patients to intervention strategies that utilize vitamin E.

Uncovering the metabolomic fingerprint of breast cancer

Metabolomics, the study of metabolites and small intermediate molecules, may play a key role in further elucidation of breast cancer. This dynamic, simultaneous assessment of thousands of metabolites allows identification of the presence, concentration and fluxes of specific metabolites, and recognition of the critical metabolic pathways recruited in carcinogenesis. Studies of tumour cell and tissue allow focused analysis on the tumour, whilst studies of biofluids have the appeal of concurrent assessment of tumour and host. Elucidation of these metabolites and pathways may provide essential insights into both the intercellular environment and host/tumour interaction, allowing recognition of new biomarkers for diagnosis and prediction of outcome, new therapy targets and novel approaches for monitoring response and toxicity. Certainly, the field of metabolomics may evolve as a valuable, complementary clinical tool. In this review, current metabolomic data in breast cancer will be presented. The dominant metabolic pathways and metabolite disturbances associated with malignant transformation of breast cells will be outlined, leading to an overview of potential clinical implications for individuals with breast cancer.

Challenges of molecular nutrition research 6: the nutritional phenotype database to store, share and evaluate nutritional systems biology studies

The challenge of modern nutrition and health research is to identify food-based strategies promoting life-long optimal health and well-being. This research is complex because it exploits a multitude of bioactive compounds acting on an extensive network of interacting processes. Whereas nutrition research can profit enormously from the revolution in ‘omics’ technologies, it has discipline-specific requirements for analytical and bioinformatic procedures. In addition to measurements of the parameters of interest (measures of health), extensive description of the subjects of study and foods or diets consumed is central for describing the nutritional phenotype. We propose and pursue an infrastructural activity of constructing the “Nutritional Phenotype database” (dbNP). When fully developed, dbNP will be a research and collaboration tool and a publicly available data and knowledge repository. Creation and implementation of the dbNP will maximize benefits to the research community by enabling integration and interrogation of data from multiple studies, from different research groups, different countries and different—omics levels. The dbNP is designed to facilitate storage of biologically relevant, pre-processed—omics data, as well as study descriptive and study participant phenotype data. It is also important to enable the combination of this information at different levels (e.g. to facilitate linkage of data describing participant phenotype, genotype and food intake with information on study design and—omics measurements, and to combine all of this with existing knowledge). The biological information stored in the database (i.e. genetics, transcriptomics, proteomics, biomarkers, metabolomics, functional assays, food intake and food composition) is tailored to nutrition research and embedded in an environment of standard procedures and protocols, annotations, modular data-basing, networking and integrated bioinformatics. The dbNP is an evolving enterprise, which is only sustainable if it is accepted and adopted by the wider nutrition and health research community as an open source, pre-competitive and publicly available resource where many partners both can contribute and profit from its developments. We introduce the Nutrigenomics Organisation (NuGO, http://www.nugo.org) as a membership association responsible for establishing and curating the dbNP. Within NuGO, all efforts related to dbNP (i.e. usage, coordination, integration, facilitation and maintenance) will be directed towards a sustainable and federated infrastructure.

Opening up the "Black Box": metabolic phenotyping and metabolome-wide association studies in epidemiology

BACKGROUND

Metabolic phenotyping of humans allows information to be captured on the interactions between dietary, xenobiotic, other lifestyle and environmental exposures, and genetic variation, which together influence the balance between health and disease risks at both individual and population levels.

OBJECTIVES

We describe here the main procedures in large-scale metabolic phenotyping and their application to metabolome-wide association (MWA) studies.

METHODS

By use of high-throughput technologies and advanced spectroscopic methods, application of metabolic profiling to large-scale epidemiologic sample collections, including metabolome-wide association (MWA) studies for biomarker discovery and identification.

DISCUSSION

Metabolic profiling at epidemiologic scale requires optimization of experimental protocol to maximize reproducibility, sensitivity, and quantitative reliability, and to reduce analytical drift. Customized multivariate statistical modeling approaches are needed for effective data visualization and biomarker discovery with control for false-positive associations since 100s or 1,000s of complex metabolic spectra are being processed.

CONCLUSION

Metabolic profiling is an exciting addition to the armamentarium of the epidemiologist for the discovery of new disease-risk biomarkers and diagnostics, and to provide novel insights into etiology, biological mechanisms, and pathways.

Individual human phenotypes in metabolic space and time

Differences between individual phenotypes are due both to differences in genotype and to exposure to different environmental factors. A fundamental contribution to the definition of the individual phenotype for clinical and therapeutic applications would come from a deeper understanding of the metabolic phenotype. The existence of unique individual metabolic phenotypes has been hypothesized, but the experimental evidence has been only recently collected. Analysis of individual phenotypes over the timescale of years shows that the metabolic phenotypes are largely invariant. The present work also supports the idea that the individual metabolic phenotype can also be considered a metagenomic entity that is strongly affected by both gut microbiome and host metabolic phenotype, the latter defined by both genetic and environmental contributions.

Effects of a prolonged standardized diet on normalizing the human metabolome

BACKGROUND

Although the effects of acute dietary interventions on the human metabolome have been studied, the extent to which the metabolome can be normalized by extended dietary standardization has not yet been examined.

OBJECTIVE

We examined the metabolic profiles of healthy human subjects after extended dietary standardization to see whether the inherent variation in the human metabolome could be decreased.

DESIGN

A cohort of 10 healthy volunteers was admitted to a clinical research center for 2 wk of dietary standardization. Daily serum and urine samples and serum samples at a 2-wk follow-up visit were collected. The samples were analyzed by (1)H nuclear magnetic resonance (NMR) spectroscopy and multivariate statistical analyses.

RESULTS

NMR spectra were collected to globally profile the higher-concentration metabolites (>micromol/L concentrations). Metabolic changes were observed in some serum samples after day 1 or the 2-wk follow-up visit. For each subject, the samples from all other days had similar profiles. The urinary metabolome reflected no effects from dietary standardization. Pooled 24-h urine samples were studied, which indicated that any normalization that does occur would do so in <24 h.

CONCLUSIONS

For both the urinary and serum metabolome, a single day of dietary standardization appears to provide all of the normalization that is achievable within the strict controls implemented in a clinical research setting. After 24 h, the subjects remain in their metabolic space; the remaining intra- and intersubject variations appear to be influenced by variables such as genetics, age, and lifestyle.

Mixture analysis by NMR as applied to fruit juice quality control

Nuclear magnetic resonance (NMR) spectroscopy is rapidly gaining importance in mixture analysis, originally driven by the pharmaceutical and nowadays also by clinical applications within metabonomics. Quality control of food-related material has very similar requirements, as it also deals with mixtures, and many of the compounds found in body fluids are analyzed as well. NMR allows analysis in two ways within one experiment: namely, targeted and untargeted. Targeted stands for the safe identification and consequent quantification of individual compounds, whereas untargeted means the detection of all deviations visible by NMR using statistical analysis based on normality models. Very important is the stability and reproducibility of the NMR instrumentation used, and this means inherent minimized system internal variance. NMR is especially suited for such requirements, as it allows detection of the smallest concentration changes of many metabolites simultaneously. High-throughput flow-injection NMR as the basis for fruit juice screening allows low cost per sample and delivers substantially more relevant information than any other method and is probably the only method to produce such results.

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results.

NMR-based multi parametric quality control of fruit juices: SGF profiling

With SGF Profiling™ we introduce an NMR-based screening method for the quality control of fruit juices. This method has been developed in a joint effort by Bruker BioSpin GmbH and SGF International e.V. The system is fully automated with respect to sample transfer, measurement, data analysis and reporting and is set up on an Avance 400 MHz flow-injection NMR spectrometer. For each fruit juice a multitude of parameters related to quality and authenticity are evaluated simultaneously from a single data set acquired within a few minutes. This multimarker/multi-aspect NMR screening approach features low cost-per-sample and is highly competitive with conventional and targeted fruit juice quality control methods.

NMR quantification of trace components in complex matrices by band-selective excitation with adiabatic pulses

The use of band-selective excitation with adiabatic pulses to rapidly obtain NMR spectra of trace components in the presence of strong signals is described, along with qualitative and quantitative examples from food matrices like olive oil and honey.

1H NMR metabolomics study of age profiling in children

Metabolic profiling of urine provides a fingerprint of personalized endogenous metabolite markers that correlate to a number of factors such as gender, disease, diet, toxicity, medication, and age. It is important to study these factors individually, if possible to unravel their unique contributions. In this study, age-related metabolic changes in children of age 12 years and below were analyzed by (1)H NMR spectroscopy of urine. The effect of age on the urinary metabolite profile was observed as a distinct age-dependent clustering even from the unsupervised principal component analysis. Further analysis, using partial least squares with orthogonal signal correction regression with respect to age, resulted in the identification of an age-related metabolic profile. Metabolites that correlated with age included creatinine, creatine, glycine, betaine/TMAO, citrate, succinate, and acetone. Although creatinine increased with age, all the other metabolites decreased. These results may be potentially useful in assessing the biological age (as opposed to chronological) of young humans as well as in providing a deeper understanding of the confounding factors in the application of metabolomics.

Mapping the interaction between the hemophore HasA and its outer membrane receptor HasR using CRINEPT-TROSY NMR spectroscopy

The first step of heme acquisition by Gram-negative pathogenic bacteria through the so-called heme acquisition system, Has, requires delivery of the heme from the extracellular hemophore protein HasA to a specific outer membrane receptor, HasR. CRINEPT-TROSY NMR experiments in DPC micelles were here used to obtain information on the intermediate HasA-HasR complex in solution. A stable protein-protein adduct is detected both in the presence and in the absence of heme. Structural information on the complexed form of HasA is obtained from chemical shift mapping and statistical analysis of the spectral fingerprint of the protein NMR spectra obtained under different conditions. This approach shows the following: (i) only three different conformations are possible for HasA in solution: one for the isolated apoprotein, one for the isolated holoprotein, and one for the complexed protein, that is independent of the presence of the heme; (ii) the structure of the hemophore in the complex resembles the open conformation of the apoprotein; (iii) the surface contact area between HasA and HasR is independent of the presence of the heme, involving loop L1, loop L2, and the beta2-beta6 strands; (iv) upon complex formation the heme group is transferred from holoHasA to HasR.

Recent advances in systemic therapy: new diagnostics and biological predictors of outcome in early breast cancer

The key to optimising our approach in early breast cancer is to individualise care. Each patient has a tumour with innate features that dictate their chance of relapse and their responsiveness to treatment. Often patients with similar clinical and pathological tumours will have markedly different outcomes and responses to adjuvant intervention. These differences are encoded in the tumour genetic profile. Effective biomarkers may replace or complement traditional clinical and histopathological markers in assessing tumour behaviour and risk. Development of high-throughput genomic technologies is enabling the study of gene expression profiles of tumours. Genomic fingerprints may refine prediction of the course of disease and response to adjuvant interventions. This review will focus on the role of multiparameter gene expression analyses in early breast cancer, with regards to prognosis and prediction. The prognostic role of genomic signatures, particularly the Mammaprint and Rotterdam signatures, is evolving. With regard to prediction of outcome, the Oncotype Dx multigene assay is in clinical use in tamoxifen treated patients. Extensive research continues on predictive gene identification for specific chemotherapeutic agents, particularly the anthracyclines, taxanes and alkylating agents.

Metabolomics tools for identifying biomarkers for neuropsychiatric diseases

The repertoire of biochemicals (or small molecules) present in cells, tissue, and body fluids is known as the metabolome. Today, clinicians utilize only a very small part of the information contained in the metabolome, as revealed by the quantification of a limited set of analytes to gain information on human health. Examples include measuring glucose or cholesterol to monitor diabetes and cardiovascular health, respectively. With a focus on comprehensively studying the metabolome, the rapidly growing field of metabolomics captures the metabolic state of organisms at the global or "-omics" level. Given that the overall health status of an individual is captured by his or her metabolic state, which is a reflection of what has been encoded by the genome and modified by environmental factors, metabolomics has the potential to have a great impact upon medical practice by providing a wealth of relevant biochemical data. Metabolomics promises to improve current, single metabolites-based clinical assessments by identifying metabolic signatures (biomarkers) that embody global biochemical changes in disease, predict responses to treatment or medication side effects (pharmachometabolomics). State of the art metabolomic analytical platforms and informatics tools are being used to map potential biomarkers for a multitude of disorders including those of the central nervous system (CNS). Indeed, CNS disorders are linked to disturbances in metabolic pathways related to neurotransmitter systems (dopamine, serotonin, GABA and glutamate); fatty acids such as arachidonic acid-cascade; oxidative stress and mitochondrial function. Metabolomics tools are enabling us to map in greater detail perturbations in many biochemical pathways and links among these pathways this information is key for development of biomarkers that are disease-specific. In this review, we elaborate on some of the concepts and technologies used in metabolomics and its promise for biomarker discovery. We also highlight early findings from metabolomic studies in CNS disorders such as schizophrenia, Major Depressive Disorder (MDD), Bipolar Disorder (BD), Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD).

Evaluation and characterization of bacterial metabolic dynamics with a novel profiling technique, real-time metabolotyping

BACKGROUND

Environmental processes in ecosystems are dynamically altered by several metabolic responses in microorganisms, including intracellular sensing and pumping, battle for survival, and supply of or competition for nutrients. Notably, intestinal bacteria maintain homeostatic balance in mammals via multiple dynamic biochemical reactions to produce several metabolites from undigested food, and those metabolites exert various effects on mammalian cells in a time-dependent manner. We have established a method for the analysis of bacterial metabolic dynamics in real time and used it in combination with statistical NMR procedures.

METHODOLOGY/PRINCIPAL FINDINGS

We developed a novel method called real-time metabolotyping (RT-MT), which performs sequential (1)H-NMR profiling and two-dimensional (2D) (1)H, (13)C-HSQC (heteronuclear single quantum coherence) profiling during bacterial growth in an NMR tube. The profiles were evaluated with such statistical methods as Z-score analysis, principal components analysis, and time series of statistical TOtal Correlation SpectroScopY (TOCSY). In addition, using 2D (1)H, (13)C-HSQC with the stable isotope labeling technique, we observed the metabolic kinetics of specific biochemical reactions based on time-dependent 2D kinetic profiles. Using these methods, we clarified the pathway for linolenic acid hydrogenation by a gastrointestinal bacterium, Butyrivibrio fibrisolvens. We identified trans11, cis13 conjugated linoleic acid as the intermediate of linolenic acid hydrogenation by B. fibrisolvens, based on the results of (13)C-labeling RT-MT experiments. In addition, we showed that the biohydrogenation of polyunsaturated fatty acids serves as a defense mechanism against their toxic effects.

CONCLUSIONS

RT-MT is useful for the characterization of beneficial bacterium that shows potential for use as probiotic by producing bioactive compounds.

The metabonomic signature of celiac disease

Celiac disease (CD) is a multifactorial disorder involving genetic and environmental factors, thus, having great potential impact on metabolism. This study aims at defining the metabolic signature of CD through Nuclear Magnetic Resonance (NMR) of urine and serum samples of CD patients. Thirty-four CD patients at diagnosis and 34 healthy controls were examined by (1)H NMR of their serum and urine. A CD patients' subgroup was also examined after a gluten-free diet (GFD). Projection to Latent Structures provided data reduction and clustering, and Support Vector Machines provided pattern recognition and classification. The classification accuracy of CD and healthy control groups was 79.7-83.4% for serum and 69.3% for urine. Sera of CD patients were characterized by lower levels (P < 0.01) of several metabolites such as amino acids, lipids, pyruvate and choline, and by higher levels of glucose and 3-hydroxybutyric acid, while urines showed altered levels (P < 0.05) of, among others, indoxyl sulfate, meta-[hydroxyphenyl]propionic acid and phenylacetylglycine. After 12 months of GFD, all but one of the patients were classified as healthy by the same statistical analysis. NMR thus reveals a characteristic metabolic signature of celiac disease. Altered serum levels of glucose and ketonic bodies suggest alterations of energy metabolism, while the urine data point to alterations of gut microbiota. Metabolomics may thus provide further hints on the biochemistry of the disease.

Metabolic profiling reveals distinct variations linked to nicotine consumption in humans--first results from the KORA study

Exposure to nicotine during smoking causes a multitude of metabolic changes that are poorly understood. We quantified and analyzed 198 metabolites in 283 serum samples from the human cohort KORA (Cooperative Health Research in the Region of Augsburg). Multivariate analysis of metabolic profiles revealed that the group of smokers could be clearly differentiated from the groups of former smokers and non-smokers. Moreover, 23 lipid metabolites were identified as nicotine-dependent biomarkers. The levels of these biomarkers are all up-regulated in smokers compared to those in former and non-smokers, except for three acyl-alkyl-phosphatidylcholines (e.g. plasmalogens). Consistently significant results were further found for the ratios of plasmalogens to diacyl-phosphatidylcolines, which are reduced in smokers and regulated by the enzyme alkylglycerone phosphate synthase (alkyl-DHAP) in both ether lipid and glycerophospholipid pathways. Notably, our metabolite profiles are consistent with the strong down-regulation of the gene for alkyl-DHAP (AGPS) in smokers that has been found in a study analyzing gene expression in human lung tissues. Our data suggest that smoking is associated with plasmalogen-deficiency disorders, caused by reduced or lack of activity of the peroxisomal enzyme alkyl-DHAP. Our findings provide new insight into the pathophysiology of smoking addiction. Activation of the enzyme alkyl-DHAP by small molecules may provide novel routes for therapy.

Perspectives for Metabolomics in Human Nutrition: An Overview

Metabolomics describes the measurement of the full complement of the products of metabolism in a single biological sample and correlating these metabolomic profiles with known physiological or pathological states. The metabolome offers the possibility of finding unique fingerprints responsible for different phenotypes. Analytical techniques such as nuclear magnetic resonance or mass spectrometry measure thousands of compounds within the metabolome simultaneously and appropriate data mining and database tools allow the finding of significant correlations between the measured metabolomes. The first direct outcome of nutritional metabolomics will be the discovery of biomarkers, which can reveal changes in health and disease but also indicate short term and long-term dietary intake. The concerted actions of nutrigenomics and metabolomics will play a crucial role in understanding how specific interactions of single nucleotide polymorphisms (SNP) influence a person's response to a diet. Finally, systems biology approaches to human nutrition combine transcriptomics, proteomics and metabolomics with the aim of understanding how diets interact within the human being.

Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10(-16) to 10(-21)). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.

Metabolomics: a tool for personalizing medicine?

The concept of personalized medicine is based upon using personal genetic information to predict individual responses to drug therapy. However, environmental factors such as diet, energy status, gut microbiota, health status and age will influence the expression of one’s genetic potential. Metabolomics data from biofluid and tissue sample analysis hold information regarding a patient’s genotype and phenotype. Metabolomics data can be rapidly collected from biofluid samples over time, providing temporal metabolic analyses of patient samples. In addition to metabolic markers of a patient’s phenotype, metabolomics can provide markers of drug efficacy, toxicity and clearance.