NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition

Metabonomics approach to determine metabolic differences between green tea and black tea consumption

The purpose of this study was to compare the effects of black and green tea consumption on human metabolism. Seventeen healthy male volunteers consumed black tea, green tea, or caffeine in a randomized crossover study. Twenty-four-hour urine and blood plasma samples were analyzed by NMR-based metabonomics, that is, high-resolution 1H NMR metabolic profiling combined with multivariate statistics. Green and black tea consumption resulted in similar increases in urinary excretion of hippuric acid and 1,3-dihydroxyphenyl-2-O-sulfate, both of which are end products of tea flavonoid degradation by colonic bacteria. Several unidentified aromatic metabolites were detected in urine specifically after green tea intake. Interestingly, green and black tea intake also had a different impact on endogenous metabolites in urine and plasma. Green tea intake caused a stronger increase in urinary excretion of several citric acid cycle intermediates, which suggests an effect of green tea flavanols on human oxidative energy metabolism and/or biosynthetic pathways.

Metabolic brain imaging by magnetic resonance

Novel magnetic resonance methods have been developed to noninvasively measure biochemical compounds in the human brain as guided by magnetic resonance imaging. Together, these methods are referred to as magnetic resonance spectroscopy (MRS) and can be divided into three major categories: single voxel MRS, magnetic resonance spectroscopic imaging and dynamic MRS, which is a novel adaption of the first method. The techniques and range of biochemical compounds that can be measured safely and serially are advancing rapidly, with many technical developments. MRS methods, when applied to the human brain, have an important diagnostic role, help monitor and guide therapeutic interventions and provide a tool to investigate the mechanisms of neuropsychiatric disease processes, normal brain development and neuropharmacology in vivo.

Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans

BACKGROUND

Metabolomics in human nutrition research is faced with the challenge that changes in metabolic profiles resulting from diet may be difficult to differentiate from normal physiologic variation.

OBJECTIVE

We assessed the extent of intra- and interindividual variation in normal human metabolic profiles and investigated the effect of standardizing diet on reducing variation.

DESIGN

Urine, plasma, and saliva were collected from 30 healthy volunteers (23 females, 7 males) on 4 separate mornings. For visits 1 and 2, free food choice was permitted on the day before biofluid collection. Food choice on the day before visit 3 was intended to mimic that for visit 2, and all foods were standardized on the day before visit 4. Samples were analyzed by using 1H nuclear magnetic resonance spectroscopy followed by multivariate data analysis.

RESULTS

Intra- and interindividual variations were considerable for each biofluid. Visual inspection of the principal components analysis scores plots indicated a reduction in interindividual variation in urine, but not in plasma or saliva, after the standard diet. Partial least-squares discriminant analysis indicated time-dependent changes in urinary and salivary samples, mainly resulting from creatinine in urine and acetate in saliva. The predictive power of each model to classify the samples as either night or morning was 85% for urine and 75% for saliva.

CONCLUSIONS

Urine represented a sensitive metabolic profile that reflected acute dietary intake, whereas plasma and saliva did not. Future metabolomics studies should consider recent dietary intake and time of sample collection as a means of reducing normal physiologic variation.

A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis

Metabonomic/metabolomic studies can involve the analysis of large numbers of samples for the detection of biomarkers and confidence in the analytical data, generated by methods such as GC and HPLC-MS, requires active measures on the part of the analyst. However, quality control for complex multi-component samples such as biofluids, where many of the components of interest in the sample are unknown prior to analysis, poses significant problems. Here the repeat analysis of a pooled sample throughout the run, thereby enabling the analysis to be monitored and controlled using targeted inspection of the data and pattern recognition, is advocated as a pragmatic solution to this problem.

Current and future applications of in vitro magnetic resonance spectroscopy in hepatobiliary disease

Nuclear magnetic resonance spectroscopy allows the study of cellular biochemistry and metabolism, both in the whole body in vivo and at higher magnetic field strengths in vitro. Since the technique is non-invasive and non-selective, magnetic resonance spectroscopy methodologies have been widely applied in biochemistry and medicine. In vitro magnetic resonance spectroscopy studies of cells, body fluids and tissues have been used in medical biochemistry to investigate pathophysiological processes and more recently, the technique has been used by physicians to determine disease abnormalities in vivo. This highlighted topic illustrates the potential of in vitro magnetic resonance spectroscopy in studying the hepatobiliary system. The role of in vitro proton and phosphorus magnetic resonance spectroscopy in the study of malignant and non-malignant liver disease and bile composition studies are discussed, particularly with reference to correlative in vivo whole-body magnetic resonance spectroscopy applications. In summary, magnetic resonance spectroscopy techniques can provide non-invasive biochemical information on disease severity and pointers to underlying pathophysiological processes. Magnetic resonance spectroscopy holds potential promise as a screening tool for disease biomarkers, as well as assessing therapeutic response.

Principal component analysis of urine metabolites detected by NMR and DESI–MS in patients with inborn errors of metabolism

Urine metabolic profiles of patients with inborn errors of metabolism were examined with nuclear magnetic resonance (NMR) and desorption electrospray ionization mass spectrometry (DESI-MS) methods. Spectra obtained from the study of urine samples from individual patients with argininosuccinic aciduria (ASA), classic homocystinuria (HCY), classic methylmalonic acidemia (MMA), maple syrup urine disease (MSUD), phenylketonuria (PKU) and type II tyrosinemia (TYRO) were compared with six control patient urine samples using principal component analysis (PCA). Target molecule spectra were identified from the loading plots of PCA output and compared with known metabolic profiles from the literature and metabolite databases. Results obtained from the two techniques were then correlated to obtain a common list of molecules associated with the different diseases and metabolic pathways. The combined approach discussed here may prove useful in the rapid screening of biological fluids from sick patients and may help to improve the understanding of these rare diseases.

Redox proteomics in some age-related neurodegenerative disorders or models thereof

Neurodegenerative diseases cause memory loss and cognitive impairment. Results from basic and clinical scientific research suggest a complex network of mechanisms involved in the process of neurodegeneration. Progress in treatment of such disorders requires researchers to better understand the functions of proteins involved in neurodegenerative diseases, to characterize their role in pathogenic disease mechanisms, and to explore their roles in the diagnosis, treatment, and prevention of neurodegenerative diseases. A variety of conditions of neurodegenerative diseases often lead to post-translational modifications of proteins, including oxidation and nitration, which might be involved in the pathogenesis of neurodegenerative diseases. Redox proteomics, a subset of proteomics, has made possible the identification of specifically oxidized proteins in neurodegenerative disorders, providing insight into a multitude of pathways that govern behavior and cognition and the response of the nervous system to injury and disease. Proteomic analyses are particularly suitable to elucidate post-translational modifications, expression levels, and protein-protein interactions of thousands of proteins at a time. Complementing the valuable information generated through the integrative knowledge of protein expression and function should enable the development of more efficient diagnostic tools and therapeutic modalities. Here we review redox proteomic studies of some neurodegenerative diseases.

Impact of Analytical Bias in Metabonomic Studies of Human Blood Serum and Plasma

Concurrent with the explosion in the number of publications reporting biomarker discovery by profiling technologies, such as proteomics and pattern recognition, has been the increase in evidence highlighting the susceptibility of these approaches to analytical and experimental bias. The work presented here addresses these timely issues by delivering a detailed characterization of the effect of common sources of bias in clinical studies on serum and plasma profiles generated by a key technology in metabonomics, NMR spectroscopy. Specifically, differences in composition when blood samples were collected onto and in the absence of ice, over a series of serum-clot contact times, the stability of NMR-prepared samples over time and the effect on the metabolic profile of freeze-thawing were examined. While differences between individuals were far greater than variation from any other experimental factor, each of the conditions examined did cause slight alterations to the NMR profile that could produce a systematic bias. Variation due to clotting time caused changes in energy metabolites, which were delayed by ice with no other spectral effects. Room-temperature stability and hence NMR spectral repeatability were high (<1% intrasample variation). Higher molecular weight species such as lipoproteins were more susceptible to the variations present in the examined factors. These observations have implications for profiling study design, and hence, our results form a new and valuable resource for those attempting clinical metabolic profiling, for regulatory agencies involved in the licensing of clinical tests and in the generation of international reporting standards for metabonomics.

Probabilistic Quotient Normalization as Robust Method to Account for Dilution of Complex Biological Mixtures. Application in 1H NMR Metabonomics

For the analysis of the spectra of complex biofluids, preprocessing methods play a crucial role in rendering the subsequent data analyses more robust and accurate. Normalization is a preprocessing method, which accounts for different dilutions of samples by scaling the spectra to the same virtual overall concentration. In the field of 1H NMR metabonomics integral normalization, which scales spectra to the same total integral, is the de facto standard. In this work, it is shown that integral normalization is a suboptimal method for normalizing spectra from metabonomic studies. Especially strong metabonomic changes, evident as massive amounts of single metabolites in samples, significantly hamper the integral normalization resulting in incorrectly scaled spectra. The probabilistic quotient normalization is introduced in this work. This method is based on the calculation of a most probable dilution factor by looking at the distribution of the quotients of the amplitudes of a test spectrum by those of a reference spectrum. Simulated spectra, spectra of urine samples from a metabonomic study with cyclosporin-A as the active compound, and spectra of more than 4000 samples of control animals demonstrate that the probabilistic quotient normalization is by far more robust and more accurate than the widespread integral normalization and vector length normalization.

Metabonomics techniques and applications to pharmaceutical research & development

In this review, the background to the approach known as metabonomics is provided, giving a brief historical perspective and summarizing the analytical and statistical techniques used. Some of the major applications of metabonomics relevant to pharmaceutical Research & Development are then reviewed including the study of various influences on metabolism, such as diet, lifestyle, and other environmental factors. The applications of metabonomics in drug safety studies are explained with special reference to the aims and achievements of the Consortium for Metabonomic Toxicology. Next, the role that metabonomics might have in disease diagnosis and therapy monitoring is provided with some examples, and the concept of pharmacometabonomics as a way of predicting an individual's response to treatment is highlighted. Some discussion is given on the strengths and weaknesses, opportunities of, and threats to metabonomics.

Metabolomic analysis using optimized NMR and statistical methods

NMR-based metabolomics requires robust automated methodologies, and the accuracy of NMR-based metabolomics data is greatly influenced by the reproducibility of data acquisition and processing methods. Effective water resonance signal suppression and reproducible spectral phasing and baseline traces across series of related samples are crucial for statistical analysis. We assess robustness, repeatability, sensitivity, selectivity, and practicality of commonly used solvent peak suppression methods in the NMR analysis of biofluids with respect to the automated processing of the NMR spectra and the impact of pulse sequence and data processing methods on the sensitivity of pattern recognition and statistical analysis of the metabolite profiles. We introduce two modifications to the excitation sculpting pulse sequence whereby the excitation solvent suppression pulse cascade is preceded by low-power water resonance presaturation pulses during the relaxation delay. Our analysis indicates that combining water presaturation with excitation sculpting water suppression delivers the most reproducible and information-rich NMR spectra of biofluids.

Metabonomic study of aristolochic acid-induced nephrotoxicity in rats

This paper describes a metabonomic study characterizing the nephrotoxicity induced by aristolochic acid (AA), a suspected kidney toxicant. For these studies, we examined the biochemical compositions of AA-treated rat urine using LC-MS and pattern recognition methods. The biochemical and histological patterns of rat groups treated with different AA sources showed distinct differences from those of the control group. Certain metabolic pathways, such as homocysteine formation and the folate cycle were significantly accelerated, while others, including arachidonic acid biosynthesis, were decreased. A subset-validation procedure using linear discriminant analysis (LDA) and selected predictive variables indicated that approximately 95% of the treated and nontreated rat urine samples were classified correctly into their respective treatment groups. The results suggested that this metabonomic approach is a promising methodology for the rapid in vivo screening of nephrotoxicity associated with ingesting multi-ingredient medicinal herb supplements, and provides a valid method for comprehending the chemical-induced perturbations in the metabolic network and the networked lesions.

Biomolecular NMR: a chaperone to drug discovery

Biomolecular NMR now contributes routinely to every step in the development of new chemical entities ahead of clinical trials. The versatility of NMR--from detection of ligand binding over a wide range of affinities and a wide range of drug targets with its wealth of molecular information, to metabolomic profiling, both ex vivo and in vivo--has paved the way for broadly distributed applications in academia and the pharmaceutical industry. Proteomics and initial target selection both benefit from NMR: screenings by NMR identify lead compounds capable of inhibiting protein-protein interactions, still one of the most difficult development tasks in drug discovery. NMR hardware improvements have given access to the microgram domain of phytochemistry, which should lead to the discovery of novel bioactive natural compounds. Steering medicinal chemists through the lead optimisation process by providing detailed information about protein-ligand interactions has led to impressive success in the development of novel drugs. The study of biofluid composition--metabonomics--provides information about pharmacokinetics and helps toxicological safety assessment in animal model systems. In vivo, magnetic resonance spectroscopy interrogates metabolite distributions in living cells and tissues with increasing precision, which significantly impacts the development of anticancer or neurological disorder therapeutics. An overview of different steps in recent drug discovery is presented to illuminate the links with the most recent advances in NMR methodology.

Probing gender-specific metabolism differences in humans by nuclear magnetic resonance-based metabonomics

The measurement of metabolite profiles that are interpreted to yield biomarkers using multivariate data analysis is now a well-established approach for gaining an improved understanding of the impact of genetic modifications, toxicological and therapeutic interventions, and exposure to stimuli (e.g., noxious agents, stressors, nutrients) on the network of transcripts, proteins, and metabolites present in cells, tissues, or whole organisms. This has been termed metabonomics. In this study, multivariate analysis of (1)H nuclear magnetic resonance (NMR) spectra of metabolite profiles of urine and plasma from 150 healthy humans revealed that in young people and/or individuals with low body mass indexes, females had higher rates of lipid biosynthesis than did males, whereas males had higher rates of protein turnover than did females. With increasing age, overall lipid biosynthesis decreased in females, whereas metabolism increasingly favored lipid synthesis over protein turnover in males. By relating the derived metabonomic data to known metabolic pathways and published biochemical data, it appears that females synthesize relatively more lipoproteins and unsaturated lipids than do males. Furthermore, the changes in lipid biosynthesis and urinary citrate excretion in females showed a positive correlation. Estrogen most likely plays an essential role in the regulation of, and communication between, protein and lipid biosynthesis by controlling pH in mitochondria and the cytoplasm and hence the observed altered citrate levels.

Metabonomic strategy for the investigation of the mode of action of the phytotoxin (5S,8R,13S,16R)-(-)-pyrenophorol using 1H nuclear magnetic resonance fingerprinting

The biochemical mode of action of (5S,8R,13S,16R)-(-)-pyrenophorol isolated from a Drechslera avenae pathotype was investigated by using metabolic fingerprinting. (1)H NMR spectra of crude leaf extracts from untreated Avena sterilis seedlings and A. sterilis seedlings treated with pyrenophorol were compared with those obtained from treatments with the herbicides diuron, glyphosate, mesotrione, norflurazon, oxadiazon, and paraquat. Multivariate analysis was carried out to group treatments according to the mode of action of the phytotoxic substances applied. Analysis results revealed that none of the herbicide treatments fitted the pyrenophorol model and indicate that the effect of the phytotoxin on A. sterilis differs than those caused by glyphosate, mesotrione, norflurazon, oxadiazon, paraquat, and diuron, which inhibit 5-enolpyruvylshikimate-3-phosphate synthase, 4-hydroxyphenyl-pyruvate-dioxygenase, phytoene desaturase, protoporphyrinogen oxidase, photosystem I, and photosystem II, respectively. The method applied, combined with appropriate data preprocessing and analysis, was found to be rapid for the screening of phytotoxic substances for metabolic effects.

Nutrition and food science go genomic

The wealth of genomic information and high-throughput profiling technologies are now being exploited by scientists in the disciplines of nutrition and food science. Diet and food components are prime environmental factors that affect the genome, transcriptome, proteome and metabolome, and this life-long interaction defines the health or disease state of an individual. For the first time the interaction of foods, and individual food constituents, with the biological systems can be defined on a molecular basis. Profiling technologies are used in basic-science applications for identifying the mode of action of foods or particular ingredients, and are similarly taken into the science-driven development of foods with a defined biofunctionality. Biomarker profiles and patterns derived from genomics applications in humans should guide nutrition and food science in developing evidence-based dietary recommendations and health-promoting foods.

Metabolic Phenotyping of Nude and Normal (Alpk:ApfCD, C57BL10J) Mice

Mice provide a range of important models of human disease. As part of a broad program of metabolic phenotyping (metabotyping) the effects of gender and strain upon urinary metabolite composition and variation have been investigated using 1H NMR spectroscopy and chemometrics in the Alpk:ApfCD, C57BL10J and the "Nude mouse". By using Principal Components Analysis (PCA) and Soft Independent Modeling by Class Analogy (SIMCA), characteristic metabotypes for each strain were produced for both male and female animals. In all three strains, female urinary metabolic profiles were characterized by higher lactate, trimethylamine-N-oxide and lower trimethylamine concentrations relative to males. Both male and female Nude mice were phenotypically distinct from the Alpk:ApfCD and C57BL10J strains-the Nude mouse phenotypes being characterized by higher urinary creatinine, guanadinoacetic acid, dimethylamine, alpha-hydroxy-N-valeric acid and taurine levels and lower hippurate, citrate, creatine and succinate concentrations relative to those excreted by the two phenotypically normal mouse strains. These data show that Nude mice exhibit a wide variety of metabolic differences across a much wider range of pathways than has previously been thought, with potentially important considerations for studies that use the Nude mouse as a mouse model.

Gas chromatography/flame ionisation detection mass spectrometry for the detection of endogenous urine metabolites for metabonomic studies and its use as a complementary tool to nuclear magnetic resonance spectroscopy

Metabonomics is a relatively new field of research in which the total pool of metabolites in body fluids or tissues from different patient groups is subjected to comparative analysis. Nuclear magnetic resonance (NMR) spectroscopy is the technology that is currently most widely used for the analysis of these highly complex metabolite mixtures, and hundreds of metabolites can be detected without any upfront separation. We have investigated in this study whether gas chromatography (GC) separation in combination with flame ionisation detection (FID) and mass spectrometry (MS) detection can be used for metabolite profiling from urine. We show that although GC sample preparation is much more involved than for NMR, hundreds of metabolites can reproducibly be detected and analysed by GC. We show that the data quality is sufficiently high--particularly if appropriate baseline correction and time-warping methods are applied--to allow for data comparison by chemometrics methods. A sample set of urines from eleven healthy human volunteers was analysed independently by GC and NMR, and subsequent chemometrics analysis of the two datasets showed some similar features. As judged by NIST database searches of the GC/MS data some of the major metabolites that are detected by NMR are also visible by GC/MS. Since in contrast to NMR every peak in GC corresponds to a single metabolite, the electron ionisation spectra can be used to quickly identify metabolites of interest if their reference spectra are present in a searchable database. In summary, we show that GC is a method that can be used as a complementary tool to NMR for metabolite profiling of urine samples.

Metabolomics in human nutrition: opportunities and challenges

Metabolomics has been widely adopted in pharmacology and toxicology but is relatively new in human nutrition. The ultimate goal, to understand the effects of exogenous compounds on human metabolic regulation, is similar in all 3 fields. However, the application of metabolomics to nutritional research will be met with unique challenges. Little is known of the extent to which changes in the nutrient content of the human diet elicit changes in metabolic profiles. Moreover, the metabolomic signal from nutrients absorbed from the diet must compete with the myriad of nonnutrient signals that are absorbed, metabolized, and secreted in both urine and saliva. The large-bowel microflora also produces significant metabolic signals that can contribute to and alter the metabolome of biofluids in human nutrition. Notwithstanding these possible confounding effects, every reason exists to be optimistic about the potential of metabolomics for the assessment of various biofluids in nutrition research. This potential lies both in metabolic profiling through the use of pattern-recognition statistics on assigned and unassigned metabolite signals and in the collection of comprehensive data sets of identified metabolites; both objectives have the potential to distinguish between different dietary treatments, which would not have been targeted with conventional techniques. The latter objective sets out a well-recognized challenge to modern biology: the development of libraries of small molecules to aid in metabolite identification. The purpose of the present review was to highlight some early challenges that need to be addressed if metabolomics is to realize its great potential in human nutrition.

Metabonomic modeling of drug toxicity

Global metabolic profiling (metabonomics/metabolomics) has shown particular promise in the area of toxicology and drug development. In both preclinical screening and mechanistic exploration, metabolic profiling can offer rapid, noninvasive toxicological information that is robust and reproducible, with little or no added technical resources to existing studies in drug metabolism and toxicity. In this review, the study design and analytical technology required for metabonomics are discussed, along with key examples of how fundamental questions in drug development can be addressed. Strategies for metabonomic data analysis in toxicity assessment are detailed in both principle and practice, together with a description of toxicologically relevant metabolic biomarkers. Extended into the assessment of efficacy and toxicity in the clinic, metabonomics may prove crucial in making personalized therapy and pharmacogenomics a reality.

Elucidating mechanisms of drug-induced toxicity

The early and high-throughput application of assays for non-genetic toxicity is of great interest to the pharmaceutical industry, although few systems have been validated as being of good predictive value. New technologies could enable toxicity to be studied in the context of systems biology. An important factor to be considered is the metabolism of drugs to reactive intermediates. Chemical reactions of these with cell and tissue nucleophiles are relatively well understood, but predicting how biological modifications will affect signalling and regulatory networks remains a challenge. Some of these pathways could be useful as sentinels for toxicity. This article will cover some examples of drug toxicity and the prospects for future technology development.

Pattern Recognition Analysis for Classification of Hypertensive Model Rats and Diurnal Variation Using 1H-NMR Spectroscopy of Urine

Urine samples were collected during the daytime and nighttime from spontaneously hypertensive model rats and normal rats without dosing. The 1H NMR spectra were measured for their urine samples, and analyzed by a pattern recognition method, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). The separation of urinary data due to the diurnal variation (daytime and nighttime) and also to the difference between the two strains of rat was achieved in the PCA score plot. Differences of the urinary profiles in the respective separation were effectively extracted as marker variables by the SIMCA method. NMR measurements coupled with pattern recognition methods provide a straightforward approach to inspect the disease metabolic status and the preliminary screening tool of marker candidates for further development.