Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics

Metabolomics in the developing human being

Metabolomics is based on the detailed analysis of metabolites and represents a unique chemical fingerprint of an organism. This approach allows assessing the dynamic behavior of biologic systems with multiple network interactions among individual components. The field of metabolic profiling has rapidly developed over the last decade, with successful applications in various research areas including toxicology, disease diagnosis and classification, pharmacology, and nutrition. This article provides a comprehensive account of existing data in the literature from animal and clinical studies on the use of metabolomics for improved understanding of medical conditions affecting the neonate and the developing human being.

Perspectives for metabolomics in testosterone replacement therapy

Testosterone is the major circulating androgen in men but exhibits an age-related decline in the ageing male. Late-onset hypogonadism or androgen deficiency syndrome (ADS) is a 'syndromic' disorder including both a persistent low testosterone serum concentration and major clinical symptoms, including erectile dysfunction, low libido, decreased muscle mass and strength, increased body fat, decreased vitality or depressed mood. Given its unspecific symptoms, treatment goals and monitoring parameters, this review will outline the various uncertainties concerning the diagnosis, therapy and monitoring of ADS to date. Literature was identified primarily through searches for specific investigators in the PubMed database. No date or language limits were applied in the literature search for the present review. The current state of research, showing that metabolomics is starting to have an impact not only on disease diagnosis and prognosis but also on drug treatment efficacy and safety monitoring, will be presented, and the application of metabolomics to improve the clinical management of ADS will be discussed. Finally, the scientific opportunities presented by metabolomics and other -omics as novel and promising tools for biomarker discovery and individualised testosterone replacement therapy in men will be explored.

Metabolomic analysis of the toxic effect of chronic low-dose exposure to acephate on rats using ultra-performance liquid chromatography/mass spectrometry

To study the toxic effect of chronic exposure to acephate at low-dose levels, a metabolomics approach based on ultra-performance liquid chromatography/mass spectrometry (UPLC-MS) was applied. Three different doses of 0.5 mg/kg/day, 1.5 mg/kg/day, and 4.5 mg/kg/day acephate were administered to Wistar rats for 24 weeks. Endogenous metabolite profiles were obtained with UPLC-MS for all rats at six time points after treatment. Some metabolites like dimethylthiophosphate and uric acid in urine were detected at week 4. Dimethylthiophosphate, which had the most significant elevations compared with other biomarkers, was considered as an early, sensitive biomarker of exposure to acephate. Moreover, there were some endogenous metabolite changes, which demonstrated that the doses of 1.5 mg/kg/day and 4.5 mg/kg/day of acephate led to renal injury and perturbed the normal metabolic processes of rats, including glucose, nucleic acid, and protein metabolism. A connection between exposure to acephate and the metabolic disturbance has been found and interpreted. Our study indicates that the metabolomics approach based on UPLC-MS of urine provides more information on toxicity than the conventional toxicological evaluation methods in measuring changes and can be considered as a promising technique for the study of the toxic effect of acephate.

Applications of NMR metabolomics to the study of foodstuffs: truffle, kiwifruit, lettuce, and sea bass

In this review, four examples of the NMR metabolomic approach to foodstuff investigation are reported. Different types of foodstuff of different origin (namely truffle, kiwifruit, lettuce, and sea bass), with different metabolite composition, processing, and storage procedures have been chosen to demonstrate the versatility and potentiality of NMR in the foodstuff analysis. Fundamental aspects of NMR methodology such as sample preparation, metabolites extraction, quantitative elaboration of spectral data, and statistical analysis have been described. Metabolic profilings of aqueous and/or organic extracts as obtained by one- and two-dimensional NMR experiments have been reported together with the results obtained from their statistical elaboration. Discrimination between wild and farmed sea bass and between genetically modified and wild lettuces as well as changes in the kiwifruit metabolic profiles monitored over the season have been investigated. For each foodstuff, some complementary findings provided by other analytical methods are also described to underline the importance of different analytical approaches to explore specific aspects related to foodstuff.

1H Nuclear Magnetic Resonance (NMR) Metabolomic Study of Chronic Organophosphate Exposure in Rats

1H NMR spectroscopy and chemometric analysis were used to characterize rat urine obtained after chronic exposure to either tributyl phosphate (TBP) or triphenyl phosphate (TPP). In this study, the daily dose exposure was 1.5 mg/kg body weight for TBP, or 2.0 mg/kg body weight for TPP, administered over a 15-week period. Orthogonal signal correction (OSC) -filtered partial least square discriminant analysis (OSC-PLSDA) was used to predict and classify exposure to these organophosphates. During the development of the model, the classification error was evaluated as a function of the number of latent variables. NMR spectral regions and corresponding metabolites important for determination of exposure type were identified using variable importance in projection (VIP) coefficients obtained from the OSC-PLSDA analysis. As expected, the model for classification of chronic (1.5-2.0 mg/kg body weight daily) TBP or TPP exposure was not as strong as the previously reported model developed for identifying acute (15-20 mg/kg body weight) exposure. The set of majorly impacted metabolites identified for chronic TBP or TPP exposure was slightly different than those metabolites previously identified for acute exposure. These metabolites were then mapped to different metabolite pathways and ranked, allowing the metabolic response to chronic organophosphate exposure to be addressed.

Metformin elicits anticancer effects through the sequential modulation of DICER and c-MYC

Diabetic patients treated with metformin have a reduced incidence of cancer and cancer-related mortality. Here we show that metformin affects engraftment and growth of breast cancer tumours in mice. This correlates with the induction of metabolic changes compatible with clear anticancer effects. We demonstrate that microRNA modulation underlies the anticancer metabolic actions of metformin. In fact, metformin induces DICER expression and its effects are severely impaired in DICER knocked down cells. Conversely, ectopic expression of DICER recapitulates the effects of metformin in vivo and in vitro. The microRNAs upregulated by metformin belong mainly to energy metabolism pathways. Among the messenger RNAs downregulated by metformin, we found c-MYC, IRS-2 and HIF1alpha. Downregulation of c-MYC requires AMP-activated protein kinase-signalling and mir33a upregulation by metformin. Ectopic expression of c-MYC attenuates the anticancer metabolic effects of metformin. We suggest that DICER modulation, mir33a upregulation and c-MYC targeting have an important role in the anticancer metabolic effects of metformin.

Investigating potential mechanisms of obesity by metabolomics

Obesity is a serious health problem with an increased risk of several common diseases including diabetes, cardiovascular disease, and cancer. Metabolomics is an emerging analytical technique for systemic determination of metabolite profiles, which is useful for understanding the biochemical changes in obesity or related diseases both in individual organs and at the organism level. Increasingly, this technology has been applied to the study of obesity, complementing transcriptomics and/or proteomics analyses. Indeed, the alterations of metabolites in biofluids/tissues are direct indicators of variations in physiology or pathology. In this paper, we will examine the obesity-related alterations in significant metabolites that have been identified by metabolomics as well as their metabolic pathway associations. Issues concerning the screening of biologically significant metabolites related to obesity will also be discussed.

Energy metabolism in nuclear reprogramming

Nuclear reprogramming with stemness factors enables resetting of somatic differentiated tissue back to the pluripotent ground state. Recent evidence implicates mitochondrial restructuring and bioenergetic plasticity as key components underlying execution of orchestrated dedifferentiation and derivation of induced pluripotent stem cells. Aerobic to anaerobic transition of somatic oxidative energy metabolism into a glycolytic metabotype promotes proficient reprogramming, establishing a novel regulator of acquired stemness. Metabolomic profiling has further identified specific metabolic remodeling traits defining lineage redifferentiation of pluripotent cells. Therefore, mitochondrial biogenesis and energy metabolism comprise a vital axis for biomarker discovery, intimately reflecting the molecular dynamics fundamental for the resetting and redirection of cell fate.

Stable isotope-resolved metabolomics and applications for drug development

Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), during the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly cancer. The metabolome is the functional readout of the genome, functional genome, and proteome; it is also an integral partner in molecular regulations for homeostasis. The interrogation of the metabolome, or metabolomics, is now being applied to numerous diseases, largely by metabolite profiling for biomarker discovery, but also in pharmacology and therapeutics. Recent advances in stable isotope tracer-based metabolomic approaches enable unambiguous tracking of individual atoms through compartmentalized metabolic networks directly in human subjects, which promises to decipher the complexity of the human metabolome at an unprecedented pace. This knowledge will revolutionize our understanding of complex human diseases, clinical diagnostics, as well as individualized therapeutics and drug response. In this review, we focus on the use of stable isotope tracers with metabolomics technologies for understanding metabolic network dynamics in both model systems and in clinical applications. Atom-resolved isotope tracing via the two major analytical platforms, NMR and MS, has the power to determine novel metabolic reprogramming in diseases, discover new drug targets, and facilitates ADME studies. We also illustrate new metabolic tracer-based imaging technologies, which enable direct visualization of metabolic processes in vivo. We further outline current practices and future requirements for biochemoinformatics development, which is an integral part of translating stable isotope-resolved metabolomics into clinical reality.

Metabolomics and bladder cancer

Diagnosis of bladder cancer is primarily made based on clinical presentation and then by direct visualization with cystoscopy. Despite the massive investments recently made to identify urinary-based assays that are able to diagnosis urothelial carcinoma, urine cytology and cystoscopy still remain the gold standard. Recently proof of principle studies have shown that noninvasive urine-based metabolomics, using high pressure liquid chromatography (HPLC) and nuclear magnetic resonance (NMR), may be able to accurately diagnosis bladder cancer. This review discusses the published studies investigating metabolomics and bladder cancer and the future potential of this developing field.

Dynamic phosphometabolomic profiling of human tissues and transgenic models by 18O-assisted ³¹P NMR and mass spectrometry

Next-generation screening of disease-related metabolomic phenotypes requires monitoring of both metabolite levels and turnover rates. Stable isotope (18)O-assisted (31)P nuclear magnetic resonance (NMR) and mass spectrometry uniquely allows simultaneous measurement of phosphometabolite levels and turnover rates in tissue and blood samples. The (18)O labeling procedure is based on the incorporation of one (18)O into P(i) from [(18)O]H(2)O with each act of ATP hydrolysis and the distribution of (18)O-labeled phosphoryls among phosphate-carrying molecules. This enables simultaneous recording of ATP synthesis and utilization, phosphotransfer fluxes through adenylate kinase, creatine kinase, and glycolytic pathways, as well as mitochondrial substrate shuttle, urea and Krebs cycle activity, glycogen turnover, and intracellular energetic communication. Application of expanded (18)O-labeling procedures has revealed significant differences in the dynamics of G-6-P[(18)O] (glycolysis), G-3-P[(18)O] (substrate shuttle), and G-1-P[(18)O] (glycogenolysis) between human and rat atrial myocardium. In human atria, the turnover of G-3-P[(18)O], which defects are associated with the sudden death syndrome, was significantly higher indicating a greater importance of substrate shuttling to mitochondria. Phosphometabolomic profiling of transgenic hearts deficient in adenylate kinase (AK1-/-), which altered levels and mutations are associated to human diseases, revealed a stress-induced shift in metabolomic profile with increased CrP[(18)O] and decreased G-1-P[(18)O] metabolic dynamics. The metabolomic profile of creatine kinase M-CK/ScCKmit-/--deficient hearts is characterized by a higher G-6-[(18)O]P turnover rate, G-6-P levels, glycolytic capacity, γ/β-phosphoryl of GTP[(18)O] turnover, as well as β-[(18)O]ATP and β-[(18)O]ADP turnover, indicating altered glycolytic, guanine nucleotide, and adenylate kinase metabolic flux. Thus, (18)O-assisted gas chromatography-mass spectrometry and (31)P NMR provide a suitable platform for dynamic phosphometabolomic profiling of the cellular energetic system enabling prediction and diagnosis of metabolic diseases states.

Quantitative UPLC-MS/MS analysis of the gut microbial co-metabolites phenylacetylglutamine, 4-cresyl sulphate and hippurate in human urine: INTERMAP Study

The role of the gut microbiome in human health, and non-invasive measurement of gut dysbiosis are of increasing clinical interest. New high-throughput methods are required for the rapid measurement of gut microbial metabolites and to establish reference ranges in human populations. We used ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) -- positive and negative electrospray ionization modes, multiple reaction monitoring transitions -- to simultaneously measure three urinary metabolites (phenylacetylglutamine, 4-cresyl sulphate and hippurate) that are potential biomarkers of gut function, among multi-ethnic US men and women aged 40-59 from the INTERMAP epidemiologic study (n = 2000, two timed 24-hr urine collections/person). Metabolite concentrations were quantified via stable isotope labeled internal standards. The assay was linear in the ranges 1ng/mL (lower limit of quantification) to 1000ng/mL (phenylacetylglutamine and 4-cresyl sulfate) and 3ng/mL to 3000ng/mL (hippurate). These quantitative data provide new urinary reference ranges for population-based human samples: mean (standard deviation) 24-hr urinary excretion for phenylacetylglutamine was: 1283.0 (751.7) μmol/24-hr (men), 1145.9 (635.5) μmol/24-hr (women); for 4-cresyl sulphate, 1002.5 (737.1) μmol/24-hr (men), 1031.8 (687.9) μmol/24-hr (women); for hippurate, 6284.6 (4008.1) μmol/24-hr (men), 4793.0 (3293.3) μmol/24-hr (women). Metabolic profiling by UPLC-MS/MS in a large sample of free-living individuals has provided new data on urinary reference ranges for three urinary microbial co-metabolites, and demonstrates the applicability of this approach to epidemiological investigations.

Advantage of Applying OSC to (1)H NMR-Based Metabonomic Data of Celiac Disease

BACKGROUND

Celiac disease (CD) is a disorder associated with body reaction to gluten. After the gluten intake, an immune reaction against the protein occurs and damages villi of small intestine in celiac patients gradually.

OBJECTIVES

The OSC, a filtering method for minimization of inter- and intra-spectrometer variations that influence on data acquisition, was applied to biofluid NMR data of CD patients.

PATIENTS AND METHODS

In this study, metabolites of total 56 serum samples from 12 CD patients, 15 CD patients taking gluten-free diet (GFD), and 29 healthy cases were analyzed using nuclear magnetic resonance (NMR) and associated theoretical analysis. Employing ProMetab (version ProMetab_v3_3) software, data obtained from NMR spectra were reduced and orthogonal signal correction (OSC) effect on celiac disease metabonomics before and after the separation by principle component analysis (PCA) was investigated.

RESULTS

The three groups were separated by OSC and findings were analyzed by partial least squares discriminant analysis (PLS-DA) method. Root mean square error of calibration (RMSEc) and correlation coefficient of calibration (Rc) for PLS-DA referred to an efficient group separation filtered by OSC.

CONCLUSIONS

The applied leave-one-out cross-validation to PLS-DA method performed along with OSC confirmed validation of data analysis. Finally four metabolites are introduced as CD biomarkers.

Magic angle spinning NMR-based metabolic profiling of head and neck squamous cell carcinoma tissues

High-resolution magic-angle spinning (HR-MAS) proton NMR spectroscopy is used to explore the metabolic signatures of head and neck squamous cell carcinoma (HNSCC) which included matched normal adjacent tissue (NAT) and tumor originating from tongue, lip, larynx and oral cavity, and associated lymph-node metastatic (LN-Met) tissues. A total of 43 tissues (18 NAT, 18 Tumor and 7 LN-Met) from 22 HNSCC patients were analyzed. Principal Component Analysis of NMR data showed a clear classification between NAT and tumor tissues, however, LN-Met tissues were classified among tumor. A partial least-squares discriminant analysis model generated from NMR metabolic profiles was used to differentiate normal from tumor samples (Q(2) > 0.80, Receiver Operator Characteristic area under the curve >0.86, using 7-fold cross validation). HNSCC and LN-Met tissues showed elevated levels of lactate, amino acids including leucine, isoleucine, valine, alanine, glutamine, glutamate, aspartate, glycine, phenylalanine and tyrosine, choline containing compounds, creatine, taurine, glutathione, and decreased levels of triglycerides. These elevated metabolites were associated with highly active glycolysis, increased amino acids influx (anaplerosis) into the TCA cycle, altered energy metabolism, membrane choline phospholipid metabolism, and oxidative and osmotic defense mechanisms. Moreover, decreased levels of triglycerides may indicate lipolysis followed by β-oxidation of fatty acids that may exist to deliver bioenergy for rapid tumor cell proliferation and growth.

Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis

Metabolic profiling of natural products is used to map correlated concentration variances of known and unknown secondary metabolites in extracts. NMR-spectroscopy is in this respect regarded as a convenient and reproducible technique with the ability to detect a wide range of small organic compounds. Two-dimensional J-resolved NMR-spectra are used in this context to resolve overlapping signals by separating the effect of J-coupling from the effect of chemical shifts. Often one-dimensional projections of these data are used as input for standard multivariate statistical methods, and only the intensity variances along the chemical shift axis are taken into account. Here, we describe the use of parallel factor analysis (PARAFAC) as a tool to preprocess a set of two-dimensional J-resolved spectra with the aim of keeping the J-coupling information intact. PARAFAC is a mathematical decomposition method that fits three-way experimental data to a model whose parameters in this case reflect concentrations and individual component spectra along the chemical shift axis and corresponding profiles along the J-coupling axis. A set of saffron samples, directly extracted with methanol-d(4), were used as a model system to evaluate the feasibility and merits of the method. To successfully use PARAFAC, the two-dimensional spectra (n = 96) had to be aligned and processed in narrow windows (0.04 ppm wide) along the chemical shift axis. Selection of windows and number of components for each PARAFAC-model was done automatically by evaluating amount of explained variance and core consistency values. Score plots showing the distribution of objects in relation to each other, and loading plots in the form of two-dimensional pseudospectra with the same appearance as the original J-resolved spectra but with positive and negative contributions are presented. Loadings are interpreted not only in terms of signals with different chemical shifts but also the associated J-coupling profiles.

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.

Metabolomic analysis reveals novel isoniazid metabolites and hydrazones in human urine

Isoniazid (INH) is a first-line drug for tuberculosis control; the side effects of INH are thought to be associated with its metabolism, and this study was designed to globally characterize isoniazid metabolism. Metabolomic strategies were used to profile isoniazid metabolism in humans. Eight known and seven novel INH metabolites and hydrazones were identified in human urine. The novel products included two hydroxylated INH metabolites and five hydrazones. The two novel metabolites were determined as 2-oxo-1,2-dihydro-pyridine-4-carbohydrazide and isoniazid N-oxide. Five novel hydrazones were produced by condensation of isoniazid with keto acids that are intermediates in the metabolism of essential amino acids, namely, leucine and/or isoleucine, lysine, tyrosine, tryptophan, and phenylalanine. This study enhances our knowledge of isoniazid metabolism and disposition and may offer new avenues for investigating INH-induced toxicity.

Xenobiotic metabolomics: major impact on the metabolome

Xenobiotics are encountered by humans on a daily basis and include drugs, environmental pollutants, cosmetics, and even components of the diet. These chemicals undergo metabolism and detoxication to produce numerous metabolites, some of which have the potential to cause unintended effects such as toxicity. They can also block the action of enzymes or receptors used for endogenous metabolism or affect the efficacy and/or bioavailability of a coadministered drug. Therefore, it is essential to determine the full metabolic effects that these chemicals have on the body. Metabolomics, the comprehensive analysis of small molecules in a biofluid, can reveal biologically relevant perturbations that result from xenobiotic exposure. This review discusses the impact that genetic, environmental, and gut microflora variation has on the metabolome, and how these variables may interact, positively and negatively, with xenobiotic metabolism.

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.

Lipidomics at the interface of structure and function in systems biology

Cells, tissues, and biological fluids contain a diverse repertoire of many tens of thousands of structurally distinct lipids that play multiple roles in cellular signaling, bioenergetics, and membrane structure and function. In an era where lipid-related disease states predominate, lipidomics has assumed a prominent role in systems biology through its unique ability to directly identify functional alterations in multiple lipid metabolic and signaling networks. The development of shotgun lipidomics has led to the facile accrual of high density information on alterations in the lipidome mediating physiologic cellular adaptation during health and pathologic alterations during disease. Through both targeted and nontargeted investigations, lipidomics has already revealed the chemical mechanisms underlying many lipid-related disease states.

NMR metabonomics for mammalian cell metabolism studies

The detailed knowledge of mammalian cell metabolism and its adjustments to different cell properties and perturbations, such as disease and drug exposure, is of enormous value in the deeper understanding of pathological processes and drug mechanisms, as well as in the development of new and improved methods for diagnosis, follow-up of disease progression and treatment response. This review covers recent developments in the use of NMR-based metabonomics to characterize cellular metabolomes and interpret them in terms of metabolic changes taking place in a wide range of situations. The analytical methodology available is briefly presented and the applications developed so far are reviewed. These include differences in cell properties (e.g., drug resistance, cell cycle stage, specific growth conditions and genetic characteristics) and changes induced in response to different perturbations (e.g., disease, drug exposure and irradiation).

Pharmacometabonomics as an effector for personalized medicine

This article introduces and reviews the concept of pharmacometabonomics, with recent experimental exemplifications of the approach being described and discussed. Pharmacometabonomics seeks to predict the response of an individual to a stimulus (e.g., drug, toxin, surgery, nutrition and so on) prior to the stimulus or other perturbation. It is an integral part of top-down systems biology which aims to improve understanding of phenotypic differences and the impact of beneficial and pathological interventions. The pharmacometabonomic concept is also integral to the understanding of mammalian-gut microbiome cometabolic interactions and their consequences, including the impact on disease and therapy. Although the subject is only at an early stage and requires further exemplification and validation, the approach has major implications for improved efficiency in drug discovery efforts, for example, by enabling more careful selection of animals in preclinical studies, for better stratification of patients in drug clinical trials and for individualized therapies. It could also find application in population-wide large cohort studies and in studies of nutrition where it would allow the elucidation of health risk factors and provide easily measured surrogate biomarkers.

Nuclear magnetic resonance (NMR)-based metabolomics

Biofluids are by far the most commonly studied sample type in metabolic profiling studies, encompassing blood, urine, cerebrospinal fluid, cell culture media and many others. A number of these fluids can be obtained at a high sampling frequency with minimal invasion, permitting detailed characterisation of dynamic metabolic events. One of the attractive properties of solution-state metabolomics is the ability to generate profiles from these fluids following simple preparation, allowing the analyst to gain a naturalistic, largely unbiased view of their composition that is highly representative of the in vivo situation. Solution-state samples can also be generated from the extraction of tissue or cellular samples that can be tailored to target metabolites with particular properties. Nuclear magnetic resonance (NMR) provides an excellent technique for profiling these fluids and is especially adept at characterising complex solutions. Profiling biofluid samples by NMR requires appropriate preparation and experimental conditions to overcome the demands of varied sample matrices, including those with high protein, lipid or saline content, as well as the presence of water in aqueous samples.

Deciphering squamous cell carcinoma using multidimensional genomic approaches

Squamous cell carcinomas (SqCCs) arise in a wide range of tissues including skin, lung, and oral mucosa. Although all SqCCs are epithelial in origin and share common nomenclature, these cancers differ greatly with respect to incidence, prognosis, and treatment. Current knowledge of genetic similarities and differences between SqCCs is insufficient to describe the biology of these cancers, which arise from diverse tissue origins. In this paper we provide a general overview of whole genome approaches for gene and pathway discovery and highlight the advancement of integrative genomics as a state-of-the-art technology in the study of SqCC genetics.

Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption

BACKGROUND

New food biomarkers are needed to objectively evaluate the effect of diet on health and to check adherence to dietary recommendations and healthy eating patterns.

OBJECTIVE

We developed a strategy for food biomarker discovery, which combined nutritional intervention with metabolic phenotyping and biomarker validation in a large-scale epidemiologic study.

DESIGN

We administered a standardized diet to 8 individuals and established a putative urinary biomarker of fruit consumption by using (1)H nuclear magnetic resonance (NMR) spectroscopic profiling. The origin of the biomarker was confirmed by using targeted NMR spectroscopy of various fruit. Excretion kinetics of the biomarker were measured. The biomarker was validated by using urinary NMR spectra from UK participants of the INTERMAP (International Collaborative Study of Macronutrients, Micronutrients, and Blood Pressure) (n = 499) in which citrus consumption was ascertained from four 24-h dietary recalls per person. Finally, dietary patterns of citrus consumers (n = 787) and nonconsumers (n = 1211) were compared.

RESULTS

We identified proline betaine as a putative biomarker of citrus consumption. High concentrations were observed only in citrus fruit. Most proline betaine was excreted < or =14 h after a first-order excretion profile. Biomarker validation in the epidemiologic data showed a sensitivity of 86.3% for elevated proline betaine excretion in participants who reported citrus consumption and a specificity of 90.6% (P < 0.0001). In comparison with noncitrus consumers, citrus consumers had lower intakes of fats, lower urinary sodium-potassium ratios, and higher intakes of vegetable protein, fiber, and most micronutrients.

CONCLUSION

The biomarker identification and validation strategy has the potential to identify biomarkers for healthier eating patterns associated with a reduced risk of major chronic diseases. The trials were registered at clinicaltrials.gov as NCT01102049 and NCT01102062.

Models of the human metabolic network: aiming to reconcile metabolomics and genomics

The metabolic syndrome, inborn errors of metabolism, and drug-induced changes to metabolic states all bring about a seemingly bewildering array of alterations in metabolite concentrations; these often occur in tissues and cells that are distant from those containing the primary biochemical lesion. How is it possible to collect sufficient biochemical information from a patient to enable us to work backwards and pinpoint the primary lesion, and possibly treat it in this whole human metabolic network? Potential analyses have benefited from modern methods such as ultra-high-pressure liquid chromatography, mass spectrometry, nuclear magnetic resonance spectroscopy, and more. A yet greater challenge is the prediction of outcomes of possible modern therapies using drugs and genetic engineering. This exposes the notion of viewing metabolism from a completely different perspective, with focus on the enzymes, regulators, and structural elements that are encoded by genes that specify the amino acid sequences, and hence encode the various interactions, be they regulatory or catalytic. The mainstream view of metabolism is being challenged, so we discuss here the reconciling of traditionally quantitative chemocentric metabolism with the seemingly 'parameter-free' genomic description, and vice versa.

Metabolite composition and bioactivity of Rhizoctonia solani sclerotial exudates

Sclerotia are vegetative structures that play a major role in survival of fungi under adverse conditions. The sclerotia of the plant pathogen Rhizoctonia solani AG2-2 IIIB exude liquid brown droplets that were evaluated for their bioactivity and toxicity against microorganisms and plant species. Also, their metabolic composition was analyzed by integrating Fourier transform ion cyclotron resonance-mass spectrometry (FT-ICR/MS), gas chromatography-MS (GC/MS), and proton nuclear magnetic resonance ((1)H NMR) spectroscopy. The results showed that exudates are complex mixtures composed of phenolics (17.40%), carboxylic acids (12.79%), carbohydrates (6.08%), fatty acids (3.78%), and amino acids (3.47%). The presence of such metabolites contributed to their antifungal and phytotoxic activities. The biological interpretation of the results highly suggests that the exudates not only have multiple roles in fungal physiology but also are a potential bioactive source with moderate toxicity. Our findings show with certainty that the integration of different analytical platforms is a powerful approach for extracting the maximum and reliable information on the metabolic composition of complex biological samples.

Profiling polar and semipolar plant metabolites throughout extraction processes using a combined solution-state and high-resolution magic angle spinning NMR approach

In metabolomic analyses, care should be exercised as to which metabolites are extracted from the sample and which remain in the residue; the remaining metabolites are typically discarded following the extraction process. In this study, nuclear magnetic resonance (NMR)-based metabolomics was used to visualize plant metabolite profiles throughout a series of repeated extraction processes. Metabolites remaining in the extraction residues of (13)C-labeled Arabidopsis thaliana were recovered by repeated extraction using methanol-d(4) and deuterium oxide. The soluble extracts and residual pellets from each step of the extraction process were analyzed by both solution-state and high-resolution magic angle spinning NMR. Metabolic profiling based on chemical shifts in two-dimensional (1)H-(13)C heteronuclear single-quantum coherence spectra allowed the elucidation of both structural and chemical properties. In addition to the metabolite profile, there appears to be a relationship between metabolite structure and behavior throughout the repeated extraction process. These approaches suggest that metabolites are not always extracted in a single step and that the distribution of metabolites in an extraction scenario cannot be predicted solely on the basis of solubility or polarity. The composition of all metabolites in cells influences the solubility of each metabolite; thus, particular attention should be paid because changes in only a portion of the metabolites could influence the entire metabolite profile in a solvent extract.

The use of hepatocytes to investigate drug toxicity

The liver is very active in metabolizing foreign compounds and the major target for toxicity caused by drugs. Hepatotoxicity may be the result of the drug itself or, more frequently, a result of the bioactivation process and the production of reactive metabolites. Prioritization of compounds based on human hepatotoxicity potential is currently a key unmet need in drug discovery, as it can become a major problem for several lead compounds in later stages of the drug discovery pipeline. Therefore, evaluation of potential hepatotoxicity represents a critical step in the development of new drugs. Cultured hepatocytes are increasingly used by the pharmaceutical industry for the screening of hepatotoxic potential of new molecules. Hepatocytes in culture retain hepatic key functions and constitute a valuable tool to identify chemically induced cellular damage. Their use has notably contributed to the understanding of mechanisms responsible for hepatotoxicity (disruption of cellular energy status, alteration of Ca(2+) homeostasis, inhibition of transport systems, metabolic activation, oxidative stress, covalent binding, etc.). Assessment of current cytotoxicity and hepatic-specific biochemical effects is limited by the inability to measure a wide spectrum of potential mechanistic changes involved in the drug-induced toxic injury. A convenient selection of endpoints allows a multiparametric evaluation of drug toxicity. In this regard, cytomic, proteomic, toxicogenomic and metabonomic approaches help to define patterns of hepatotoxicity for early identification of potential adverse effects of the drug to the liver.