Metabonomics: a platform for studying drug toxicity and gene function

Quality control and discrimination of pericarpium citri reticulatae and pericarpium citri reticulatae viride based on high-performance liquid chromatographic fingerprints and multivariate statistical analysis

High-performance liquid chromatographic (HPLC) fingerprints of pericarpium citri reticulatae (PCR) and pericarpium citri reticulatae viride (PCRV) were firstly measured for deliberately collected 39 authentic samples and 21 commercial samples. Both correlation coefficients of similarity for chromatograms and absolute peak areas of characteristic compounds were calculated for quantitative expression of the HPLC fingerprints. After principal component analysis (PCA) successfully distinguished the 'mixed peels' samples from authentic samples, partial least squares-linear discrimination analysis (PLS-LDA) was then effectively applied to class separation between authentic PCR and PCRV. Furthermore, the unequivocally determined compounds, hesperidin, nobiletin and tangeretin, were screened out by loadings plots of PCA and PLS-LDA. The results indicated that they could be used as chemical markers for discrimination among different groups of samples. The proposed method shows an efficient strategy for quality control of PCR and PCRV, which cannot only distinguish the 'mixed peels' but also discriminate authentic PCR and PCRV. This method has potential perspective for quality control of traditional Chinese medicine (TCM).

Concentration profiling in rat tissue by high-resolution magic-angle spinning NMR spectroscopy: investigation of a model drug

The utility of high-resolution magic-angle spinning (HR-MAS) NMR for studying drug delivery in whole tissues was explored by dosing female Sprague-Dawley rats with topical or injectable benzoic acid (BA). In principle, HR-MAS NMR permits the detection of both intra- and extracellular compounds. This is an advantage over the previous detection of topically applied BA using microdialysis coupled to HPLC/UV as microdialysis samples only the extracellular space. Skin and muscle samples were analyzed by (1)H HR-MAS NMR, and BA levels were determined using an external standard solution added to the sample rotor. One to two percent of the BA topical dose was detected in the muscle, showing that BA penetrated through the dermal and subcutaneous layers. Since BA was not detected in the muscle in the microdialysis studies, the NMR spectra revealed the intracellular localization of BA. The amount of BA detected in muscle after subcutaneous injection correlated with the distance from the dosing site. Overall, the results suggest that HR-MAS NMR can distinguish differences in the local concentration of BA varying with tissue type, dosage method, and tissue proximity to the dosing site. The results illustrate the potential of this technique for quantitative analysis of drug delivery and distribution and the challenges to be addressed as the method is refined.

Tobacco smoke particulate matter chemistry by NMR

The submicron liquid droplets constituting the particulate matter of mainstream tobacco smoke (PMMTS) are viscous and of a composition that is complex and poorly understood. PMMTS is often approximately 80% w/w 'tar' where 'tar'=total PMMTS- (nicotine+water). Many of the chemical agents in MTS responsible for smoking-related cancers are found at least partially in the PMMTS portion of MTS. The properties of PMMTS vary with brand and with puffing patterns. The chemical forms and total levels of nicotine, the identities/levels of other compositionally dominant compounds, and the identities/levels of carcinogens are of interest. Most studies of the composition of PMMTS have involved extraction then chromatography. Such methods allow the determination of low-level constituents, but alter the samples such that direct information regarding chemical conditions within the PMMTS cannot be obtained. Here, we utilize nuclear magnetic resonance spectroscopy (NMR) to examine native PMMTS in conventional cigarettes, including measurements of the brand-dependent fraction of PMMTS nicotine that is in the free-base form (increasing this fraction in inhaled tobacco smoke affects the rates of the processes governing nicotine deposition in the respiratory tract, and so has implications for smoking behavior and addiction). We also demonstrate the use of NMR for characterizing the composition of PMMTS (including the levels of selected cigarette additives) when the cosolvent DMSO-d6 is added to improve spectral resolution. The native and solvent-assisted results open the door to a range of future studies.

Mapping translational research in personalized therapeutics: from molecular markers to health policy

Translational research is frequently used in the bioscience literature to refer to the translation of basic science into practical applications at the point of patient care. With the introduction of theragnostics, a new medical subspecialty that fuses therapeutics and diagnostic medicine with the goal of providing individualized pharmacotherapy, we suggest that the focus of translational research is shifting. We identify two bottlenecks or gaps in translational research for theragnostics: GAP1 translation from basic science to first-in-human proof-of-concept; and GAP2 translation from clinical proof-of-concept to development of evidence-based personalized treatment guidelines. GAP1 translational research in theragnostics is usually performed in traditional craft-based studies with small sample sizes and led by independent academic or industry researchers. In contrast, GAP2 translational investigations typically rely on large research consortiums and population-based biobanks that couple biomarker information with longitudinal ‘real-life’ observational data on a broad range of pharmacological phenotypes. Despite an abundance of research on the use of biobanks in disease gene discovery, there has been little conceptual work on whether and to what extent population biobanks can be utilized for translating genomics discoveries to practical treatment guidelines for theragnostic tests.

1H NMR Monitoring of the Canine Metabolic Profile after Oral Administration of Xenobiotics Using Multivariate Statistics

This study describes a chemometric analysis of 1H NMR spectra of canine plasma following oral single dose administrations of two food components (lycopene and oleuropein) and of two drug products, Fungoral (ketoconazole) and Adalat (nifedipine). Due to the high interday physiological variation, 1H NMR plasma data were first filtered (by applying orthogonal signal correction) and then subjected to principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). A distinct discrimination was achieved between samples obtained with and without administration of xenobiotics using both techniques. According to distance to model criterion and as shown by the Cooman's plots there was no overlap between the four models which proved to be specific for each xenobiotic. Moreover, it was shown that this approach has the potential to recognize subtle variations in the metabolic profile even if the administered xenobiotic itself could not be detected in the canine plasma by conventional HPLC methods.

Designer genes: Filling the gap in transplantation

Accelerated evolution of the field of functional genomics has been greatly facilitated by high-throughput microarray-based gene function studies, relating to the parallel and serial expression measurements of genomes. Microarray experimentation is being applied for the study of basic research questions, drug target discovery, pharmacology, toxicogenomics, target selectivity, disease biomarker determination, development of prognostic tests, and disease subclass determination. This article will review the current applications of microarray technology in the field of organ transplantation and discuss the potential impact of this technology on transplantation medicine.

Environmental Metabolomics: A SWOT Analysis (Strengths, Weaknesses, Opportunities, and Threats)

Metabolomic approaches have the potential to make an exceptional contribution to understanding how chemicals and other environmental stressors can affect both human and environmental health. However, the application of metabolomics to environmental exposures, although getting underway, has not yet been extensively explored. This review will use a SWOT analysis model to discuss some of the strengths, weaknesses, opportunities, and threats that are apparent to an investigator venturing into this relatively new field. SWOT has been used extensively in business settings to uncover new outlooks and identify problems that would impede progress. The field of environmental metabolomics provides great opportunities for discovery, and this is recognized by a high level of interest in potential applications. However, understanding the biological consequence of environmental exposures can be confounded by inter- and intra-individual differences. Metabolomic profiles can yield a plethora of data, the interpretation of which is complex and still being evaluated and researched. The development of the field will depend on the availability of technologies for data handling and that permit ready access metabolomic databases. Understanding the relevance of metabolomic endpoints to organism health vs adaptation vs variation is an important step in understanding what constitutes a substantive environmental threat. Metabolomic applications in reproductive research are discussed. Overall, the development of a comprehensive mechanistic-based interpretation of metabolomic changes offers the possibility of providing information that will significantly contribute to the protection of human health and the environment.

Toxicogenomics: a pivotal piece in the puzzle of toxicological research

Toxicogenomics, resulting from the merge of conventional toxicology with functional genomics, being the scientific field studying the complex interactions between the cellular genome, toxic agents in the environment, organ dysfunction and disease state. When an organism is exposed to a toxic agent the cells respond by altering the pattern of gene expression. Genes are transcribed into mRNA, which in turn is translated into proteins that serve in a variety of cellular functions. Toxicogenomics through microarray technology, offers large-scale detection and quantification of mRNA transcripts, related to alterations in mRNA stability or gene regulation. This may prove advantageous in toxicological research. In the present review, the applications of toxicogenomics, especially to mechanistic and predictive toxicology are reported. The limitations arising from the use of this technology are also discussed. Additionally, a brief report of other approaches, using other -omic technologies (proteomics and metabonomics) that overcome limitations and give global information related to toxicity, is included.

Metabolic and functional profiling of the normal rat retina

We established a metabolic and functional profile map of the normal rat retina, given the premise that: 1) amino acid neurochemistry reflects metabolic integrity and cellular identity, and 2) the permeation of a cation channel probe, agmatine (1-amino-4-guanidobutane, AGB), reflects cation channel functionality. The purpose was to provide a unique method of simultaneously assessing the metabolic and functional characteristics of the normal retina, upon which a comparison can be made to disease models. Quantitative pattern recognition analysis of overlapping amino acid and AGB expression profiles was used to provide a statistically robust classification of all neural elements according to their metabolic and functional characteristics. This classification was spatially complete and with single-cell resolution. The resulting classification demonstrated 28 statistically separable theme classes dominated by characteristic glutamate, GABA, glycine, and/or taurine profiles, with each of the neuronal theme classes containing further subtypes. The inclusion of a functional parameter (AGB mapping) in the classification process nearly doubled the number of neural elements that could be ascribed a neurochemical/cation profile, compared to when amino acid labeling was used alone. Strong endogenous glutamate gated AGB labeling was observed in horizontal cells, rod bipolar cells, cholinergic amacrine cells, and AII amacrine cells. The resulting amino acid and AGB profile matrix constitutes a nomogram for assessing cellular responses to experimental challenges in models of ocular disease.

Revealing the metabolome of animal tissues using 1H nuclear magnetic resonance spectroscopy

The measurement of tissue-specific metabolic fingerprints can be of particular interest when investigating disease processes, mechanisms of toxicity, or when knowledge of the metabolic interactions between different organs is required. This chapter presents several optimized protocols for the extraction of metabolites from animal tissues, their analysis by 1H nuclear magnetic resonance (NMR) spectroscopy, and the subsequent spectral preprocessing required for an NMR-based metabolomics experiment. First, the three critical steps in the preparation of tissue extracts for NMR analysis are described, including both a perchloric acid protocol for the extraction of polar metabolites, and a methanol:chloroform protocol for extraction of polar and lipophilic metabolites. Then a series of NMR experiments are described including a standard one-dimensional (1D) 1H NMR study, a 1D 1H Carr-Purcell-Meiboom-Gill spin-echo experiment, and a two-dimensional 1H-1H J-resolved NMR experiment. The advantages and limitations of each experiment for metabolomics research are discussed. Analysis of the resulting NMR datasets is typically conducted in two phases comprising "low level" spectral preprocessing and "high level" multivariate analysis. NMR spectral preprocessing is a critical step that converts raw NMR spectra into an appropriate data format for multivariate analysis. A detailed protocol for preprocessing NMR data, using ProMetab software, is presented. Because a plethora of algorithms exist for multivariate analyses, which can be used to construct classification models or for biomarker discovery, this is beyond the scope of the current chapter.

A pharmaco-metabonomic study on the therapeutic basis and metabolic effects ofEpimedium brevicornum Maxim. on hydrocortisone-induced rat using UPLC-MS

This paper describes the pharmaco-metabonomic study on Epimedium brevicornum Maxim. treated rats with a pathologic condition similar to the 'kidney deficiency syndromes' in traditional Chinese medicine and its therapeutic basis. UPLC-MS technique was used for the development of chemical profile of Epimedium brevicornum Maxim. and endogenous metabolite profiles of rats pre- and post-hydrocortisone interfered and treated with this herbal medicine. The comparison among profiles was performed with a statistical technique, principle component analysis (PCA). Significant difference in endogenous metabolite profiles was observed in the intervention rats and the abnormality of metabolism recovered towards the normal level after administration with Epimedium brevicornum Maxim. extract. Four active constituents of Epimedium brevicornum Maxim. were found into the blood circulation of kidney-deficient rats and two of their metabolites in the urine. This work suggests that the metabonomic approach is a potentially powerful tool to explore the therapeutic basis and to clarify the possible action mechanism of traditional Chinese medicine.

Monitoring Diet Effects via Biofluids and Their Implications for Metabolomics Studies

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

Nuclear Magnetic Resonance Metabonomics

Nuclear magnetic resonance (NMR)-based metabonomics is gaining popularity in drug discovery and development and in academia in a variety of settings, ranging from toxicology, preclinical, and clinical approaches to nutrition research, studies on microorganisms, and research on plants. This chapter focuses on the basic steps in a metabonomics study and emphasizes experience and lessons learned in our lab where we focused on metabonomic analyses of plant extracts, cell lines, and a variety of animal tissues and biofluids. We emphasize that a comprehensive and suitable study design is pivotal for a correct biological interpretation of the results, as well as highly controlled experimental conditions. Sample preparation and NMR protocols are detailed for a wide range of sample types. We discuss alternative data processing strategies and considerations for a general data analysis approach, paying particular attention to the statistical interpretation and validation of the results while also highlighting approaches to avoid possible pitfalls resulting from systematic and random errors. A tutorial written for the R statistical package and other small utilities are available from the authors upon request.

[Advances in "omics" technologies for toxicological research]

Toxicology research can be applied to evaluate potential human health risks resulting from exposure to chemicals and other factors in the environment. The tremendous advances that have been made in high-throughput "omics" technologies (e.g., genomics, transcriptomics, proteomics and metabolomics) are providing good tools for toxicological research. Toxicogenomics is the study of changes in gene expression, protein and metabolite profiles, and combines the tools of traditional toxicology with those of genomics and bioinformatics. In particular, identification of changes in gene expression using DNA microarrays is an important method for understanding toxicological processes and obtaining an informative biomarker. Although these technologies have emerged as a powerful tool for clarifying hazard mechanisms, there are some concerns for the application of these technologies to toxicological research. This review summarizes the impact of "omics" technologies in toxicological study, followed by a brief discussion of future research.

Metabolomic biomarkers: their role in the critical path

Global metabolic profiling is being applied to identify biomarkers of health. Some small molecules are exquisitely sensitive indicators of health status. Metabolic profiling analyses are being used to determine biomarkers of drug safety and effectiveness as well as disease diagnosis and prognosis including organ transplant rejection. To understand the mechanism(s) of drug toxicity and disease, a systems biology approach that considers the information generated from metabolic profiling, genetics, transcriptomics and proteomics research paradigms is necessary. This will allow for a better understanding of the mechanism(s) of drug interactions and disease while possibly identifying susceptible populations, an important goal in the move toward personalized medicine.:

Metabolism and metabolomics of eukaryotes living under extreme conditions

Treatises on extremophiles are frequently focused on organisms belonging to the Archaea and Eubacteria kingdoms. However, a significant number of eukaryotes, both unicellular and multicellular, have evolved to live and thrive in extreme environments. Although less is known about eukaryotic life in extreme environments in comparison to prokaryotic extremophiles, advances in genomics and in comprehensive, high-throughput metabolic profiling techniques have provided new insight into the metabolic adaptations of eukaryotes living under extreme conditions. In this review, we will provide an overview of extremophilic life forms with emphasis on eukaryotes and we will compare metabolic adaptations in different eukaryotic extremophiles to identify generalities and specializations in adaptation to life under extreme conditions. Special emphasis will be devoted to the thermoacidophilic unicellular red alga Galdieria sulphuraria (Cyanidiaceae) as one example of a eukaryotic extremophile.

An integrated metabonomic approach to describe temporal metabolic disregulation induced in the rat by the model hepatotoxin allyl formate

The time-related metabolic events in rat liver, plasma, and urine following hepatotoxic insult with allyl formate (75 mg/kg) were studied using a combination of high-resolution liquid state and magic angle spinning (MAS) nuclear magnetic resonance (NMR) spectroscopic methods together with pattern recognition analysis. The metabonomics results were compared with the results of conventional plasma chemistry and histopathological assessments of liver damage. Various degrees of liver damage were observed in different animals, and this variation was reflected in all of the analyses. Furthermore, each analysis revealed a high degree of functional and structural recovery by the end of the study. The allyl formate-induced changes included hepatocellular necrosis, hepatic lipidosis, decreased liver glycogen and glucose, decreased plasma lipids, increased plasma creatine and tyrosine, increased urinary taurine and creatine, and decreased urinary TCA cycle intermediates. The observed reductions in hepatic glycogen and glucose suggest increased glucose utilization and are consistent with the expected depletion of hepatic ATP following mitochondrial impairment, assuming that there is a consequent increase in energy production from glycolysis. The increase in plasma tyrosine is consistent with impaired protein synthesis, a known consequence of ATP depletion. Partial least squares-based cross-correlation of the variation in the liver and plasma NMR profiles indicated that the allyl formate-induced increase in liver lipids correlated with the decrease in plasma lipids. This suggests disruption in lipid transport from the liver to plasma, which could arise through impaired apolipoprotein synthesis, as with ethionine.

Genetics and diagnostic refinement

For many psychiatric conditions it is speculated that, rather than being single disease entities, they are a set of several disorders sharing clinical features but having (partly) different underlying causes. The possibility of measuring genetic variation on a large scale has given researchers new hope of identifying these disease subtypes that may differ with respect to prognosis, course, and response to treatment. However, although a considerable number of articles have been published suggesting that we may even be on the verge of making genotype-based diagnoses, the reality is that we do not have a good answer to even the most basic question of how measured genes could best be used to refine diagnostic categories. In this article, we show that for common psychiatric disorders, it may not be possible to simply look for similar genetic profiles in groups of patients. Instead, we propose a model assuming that genotypes affect phenotypes through more or less coherent etiological systems or pathogenic processes and argue that these etiological systems may provide a more fruitful basis for defining disease subtypes. Several examples from the literature that support the face validity of different aspects of our model are given. Finally, we argue that, given our limited knowledge of disease etiology, the use of discovery-oriented techniques requiring extensive data collection and (artificial) intelligent computer searches may be imperative, and discuss the prospect of model-based diagnosis to classify etiologically different disease subtypes.

Metabolomic fingerprinting of plant extracts

The standardization and quality control of plant extracts is an important topic, in particular, when such extracts are used for medicinal purposes. Consequently, the development of fast and effective analytical methods for metabolomic fingerprinting of plant extracts is of high interest. In this investigation, electrospray mass spectrometry (ESI-MS) and (1)H NMR techniques were employed with further statistical analyses of the acquired data. The results showed that negative ion mode ESI-MS is particularly effective for characterization of plant extracts. Different samples of the same species appear well-clustered and separated from the other species. To verify the effectiveness of the method, two other batches of extracts from a species, in which the principal components were already identified (Cynara scolymus), were analyzed, and the components that were verified by the principal component analysis (PCA) were found to be within the region identified as characteristic of Cynara Scolymus extracts. The data from extracts of the other species were well separated from those pertaining to the species previously characterized. Only the case of a species that was strictly correlated from a botanical point of view, with extracts that were previously analyzed, showed overlapping.

Metabolomic approaches to phenotype characterization and applications to complex diseases

Metabolites are the key regulators of systems homeostasis. As such, concentration changes of specific groups of metabolites may reflect systemic responses to environmental, therapeutic or genetic interventions. Thus, the study of metabolites is a powerful tool for the characterization of complex phenotypes as well as for the development of biomarkers for specific physiological responses. Therefore, metabolomics is a valuable platform for studies of complex diseases and the development of new therapies, both in nonclinical disease model characterization and clinical settings.

Application of metabonomics in a compound ranking study in early drug development revealing drug-induced excretion of choline into urine

Selecting drug candidates based on toxicity is an important step in early drug development. In this case study, it is shown how metabonomics is applied to a ranking study, in which drug candidates with equal pharmacological activities are selected based on least toxic side effects. The metabonomic analyses were carried out on an animal study that followed an established protocol for pilot toxicology/ranking studies in rats, however, not specifically modified for a metabonomic assessment. It is shown how conditions not specificially adopted for metabonomics investigations can significantly influence the metabolic profiles recorded by NMR. Furthermore, it is shown how the multivariate analysis of the NMR spectra identified an extreme excretion of an endogenous metabolite into urine induced by two out of the five drug candidates. The subsequent structure elucidation by two-dimensional NMR experiments and a subsequent validation by spiking experiments identified the metabolite as choline. The discussion of the mechanistic background for the excretion of choline, which is usually well-conserved in the body, results in two hypotheses of either a massive degradation of cell membranes or an inhibition of the choline oxidation. Although the validation of these hypotheses needs a follow-up study, the finding of a increased excretion of the important metabolite choline warrants exclusion of these two compounds as viable drug candidates from a metabonomics point of view.

Urinary profiling investigation of metabolites withcis-diol structure from cancer patients based on UPLC-MS and HPLC-MS as well as multivariate statistical analysis

Nucleosides are an important class of metabolites and have been investigated as potential tumor biomarkers. A method based on ultra peerformance liquid chromatography (UPLC)-TOF MS was developed to analyze urinary nucleosides and other metabolites with cis-diol structure to distinguish between cancer patients and healthy persons and compare the results with those obtained by HPLC. The data showed that the UPLC method used about one third of the time required by HPLC and achieved a much better chromatographic resolution and increased sensitivity, the number of peaks detected by UV being 79 and 94 for HPLC and UPLC, respectively. With UPLC-TOF MS, more information was obtained about metabolites, the separation of cancer patients from healthy persons was significantly improved, and more potential biomarkers were found. The method based on UPLC-TOF MS is a powerful technique for the study of metabolite profiles.

Metabolomic analysis of methyl jasmonate treated Brassica rapa leaves by 2-dimensional NMR spectroscopy

The metabolomic analysis of Brassica rapa leaves treated with methyl jasmonate was performed using 2-dimensional J-resolved NMR spectroscopy combined with multivariate data analysis. The principal component analysis of the J-resolved NMR spectra showed discrimination between control and methyl jasmonate treated plants by principal components 1 and 2. While the level of glucose, sucrose and amino acids showed a decrease after methyl jasmonate treatment, hydroxycinnamates and glucosinolate were highly increased. Methyl jasmonate treatment resulted in a long-term accumulation of indole glucosinolate and indole-3-acetic acid, lasting up to 14 days after treatment. Malate conjugated hydroxycinnamates also exhibited an increase until 14 days after methyl jasmonate treatment, these compounds might play an important role in plant defence responses mediated by methyl jasmonate.

Application of ethyl chloroformate derivatization for gas chromatography-mass spectrometry based metabonomic profiling

A new combined gas chromatography and mass spectrometry (GC-MS) method has been developed suitable for the urine sample treatment in aqueous phase with ethyl chloroformate (ECF) derivatization agents. The method has been extensively optimized and validated over a broad range of different compounds and urine samples. Analysis of test metabolite derivatives, containing spiked standards, or rat urine exhibited acceptable linearity, satisfactory intra-batch precision (repeatability) and stability, relative standard deviations (R.S.D.) less than 10 and 15% within 48 h, respectively. The quantification limits were 150-300 pg on column for most metabolites. Recovery of several representative compounds, at different concentrations, ranged from 70 to 120%, with R.S.D. better than 10% for rat urine. We were able to generally eliminate potentially confounding variables such as medium complexity, different urea concentrations, and/or derivatization procedure variability. Metabonomic profiling of 1,2-dimethylhydrazine (DMH)-induced precancerous colon rat urine using GC-MS with ECF derivatization was performed to evaluate the proposed method. The analytical variation of the method was smaller than the biological variation in the rat urine samples, proving the suitability of the method to analyze differences in the metabonome of a living system with perturbed metabolic network. Thus, the proposed GC-MS analytical method is reliable to analyze a large variety of metabolites and can be used to investigate human pathology including disease onset, progression, and mortality.

Monitoring the health to disease continuum with global metabolic profiling and systems biology

Global metabolic profiling, which includes both metabolomics and metabonomics studies, is the latest ‘omics’ research platform that is being applied to understand the health and disease continuum. Metabolic profiling analyses have been demonstrated for the investigation of inborn errors of metabolism, organ transplant rejection, drug toxicity, disease diagnosis and prognosis, drug efficacy and nutritional status. Combining information generated from a metabolic profiling platform with that obtained based on genetics, transcriptomics and proteomics research paradigms will pave the way for a better understanding of the mechanisms of disease and toxicity. Metabolomics and nutrition will lay the groundwork for the application of personalized medicine in the 21st century.

Metabolic profiling technologies for biomarker discovery in biomedicine and drug development

The state-of-the-art of nuclear magnetic resonance spectroscopy, mass spectrometry and statistical tools for the acquisition and evaluation of complex multidimensional spectroscopic data in metabolic profiling is reviewed in this article. The continuous evolution of the sensitivity, precision and throughput has made these technologies powerful and extremely robust tools for application in systems biology, pharmaceutical and diagnostics research. Particular emphasis is also given to the collection and storage of biological samples that are subjected to metabolite profiling. Selected examples from preclinical and clinical applications are paradigmatically shown. These illustrate the power of the profiling technologies for characterizing the metabolic phenotype of healthy, diseased and treated subjects. The complexity of disease and drug treatment is asking for an adequate response by integrated and comprehensive metabolite profiling approaches that allow the discovery of new combinations of metabolic biomarkers.

Metabolic consequences of p300 gene deletion in human colon cancer cells

Metabolite profiling using (1)H nuclear magnetic resonance (NMR) spectroscopy was used to investigate the metabolic changes associated with deletion of the gene for the transcriptional coactivator p300 in the human colon carcinoma cell line HCT116. Multivariate statistical methods were used to distinguish between metabolite patterns that were dependent on cell growth conditions and those that were specifically associated with loss of p300 function. In the absence of serum, wild-type cells showed slower growth, which was accompanied by a marked decrease in phosphocholine concentration, which was not observed in otherwise isogenic cell lines lacking p300. In the presence of serum, several metabolites were identified as being significantly different between the two cell types, including glutamate and glutamine, a nicotinamide-related compound and glycerophosphocholine (GPC). However, in the absence of serum, these metabolites, with the exception of GPC, were not significantly different, leading us to conclude that most of these changes were context dependent. Transcript profiling, using DNA microarrays, showed changes in the levels of transcripts for several enzymes involved in choline metabolism, which might explain the change in GPC concentration. Localized in vivo (1)H NMR measurements on the tumors formed following s.c. implantation of these cells into mice showed an increase in the intensity of the peak from choline-containing compounds in the p300(-) tumors. These data show that NMR-based metabolite profiling has sufficient sensitivity to identify the metabolic consequences of p300 gene deletion in tumor cells in vitro and in vivo.

Can personalized drug therapy be achieved? A closer look at pharmaco-metabonomics

Between 1930 and 1990, several dozen high-penetrance, predominantly monogenic disorders were identified and characterized, which led some investigators to speculate that individualized drug treatment was just around the corner. Informative DNA tests were sought to determine genetic predisposition to toxicity and cancer, thereby identifying individuals in which a drug was likely to be effective and those at increased risk of drug toxicity. These assays represent the leading edge of phenotype-genotype association studies, which are a major goal of clinical pharmacology and pharmacogenomics. Because of the complexity of the genome, however, the task is more challenging than anticipated originally. In the past decade we have come to appreciate how difficult it is to determine unequivocally either an exact phenotype or genotype. In the near future it seems unlikely that, by themselves, either transcriptomics or proteomics will be particularly helpful in achieving individualized drug therapy. However, recent advances in metabonomics are exciting and show promise. In the future, and perhaps in combination with proteomics, metabonomics might complement genomics in achieving personalized drug therapy.

Peak alignment of urine NMR spectra using fuzzy warping

Proton nuclear magnetic resonance (1H NMR) spectroscopic analysis of mixtures has been used extensively for a variety of applications ranging from the analysis of plant extracts, wine, and food to the evaluation of toxicity in animals. For example, NMR analysis of urine samples has been used extensively for biomarker discovery and, more simply, for the construction of classification models of toxicity, disease, and biochemical phenotype. However, NMR spectra of complex mixtures typically show unwanted local peak shifts caused by matrix and instrument variability, which must be compensated for prior to statistical analysis and interpretation of the data. One approach is to align the spectral peaks across the data set. An efficient and fast warping algorithm is required as the signals typically contain ca. 32,000-64,000 data points and there can be several thousand spectra in a data set. As demonstrated in our study, the iterative fuzzy warping algorithm fulfills these requirements and can be used on-line for an alignment of the NMR spectra. Correlation coefficients between the aligned and target spectra are used as the evaluation function for the algorithm, and its performance is compared with those of other published warping methods.

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.

Effect of PA-MSHA vaccine on plasma phospholipids metabolic profiling and the ratio of Th2/Th1 cells within immune organ of mouse IgA nephropathy

Phospholipids as a class of important constituents in the biomembranes have been paid increasing attention in many fields. IgA nephropathy is now generally known to be the most common form of primary glomerulonephritis in the world. However, phospholipids metabolism in IgA nephropathy was not clear. Until recently, there was no effective treatment available for patients with IgA nephropathy. In this paper, effect of PA-MSHA vaccine on plasma phospholipid metabolic profile of mouse IgA nephropathy was investigated using high performance liquid chromatography/mass spectrometry (HPLC/MS) and principal components analysis (PCA). Female Balb/c mice were divided into four groups: model group, control group, PA-MSHA treatment group and medicine control group (dipyridamole+common threewingnut root). The experimental IgA nephropathy model was established by the immunity combination method of oral BSA and injection of SEB. It was found that combination of LC/MS technology with PCA can be successfully applied to phospholipids profile analysis, clearly classify the model group and normal group, and PA-MSHA treatment group is closer to the normal control group than medicine control group. The result showed that Th(2)/Th(1) (=CD(4)(+)CD(30)(+)/CD(4)(+)CD(30)(-)) of the model group is 20.70+/-3.57, which is significantly higher than that of the control group (1.34+/-0.14) (P<0.001). The Th(2)/Th(1) ratio of the PA-MSHA treatment group and the medicine control group are lower than that of the model group (P<0.01). It is suggested that mouse IgA nephropathy has the phospholipids metabolic abnormality, PA-MSHA vaccine cannot only regulate the abnormal phospholipids metabolism mouse with the IgA nephropathy, but also correct the over unbalance of Th(2)/Th(1) proportion.

Experimental metabonomic model of dietary variation and stress interactions

Stress in the form of moderate periods of maternal separation of newborn rats has been postulated to cause permanent changes in the central nervous system and diseases in later life. It is also considered that dietary supplementation with long chain polyunsaturated fatty acids (LC-PUFAs) can potentially ameliorate the effects of stress. The metabolic consequences of early life maternal separation stress were investigated in rats (2-14 days after birth), either alone or in combination with secondary acute water avoidance stress at 3-4 months of age. The effect of a LC-PUFA-enriched dietary intervention in stressed animals was also assessed. Systematic changes in metabolic biochemistry were evaluated using 1H nuclear magnetic resonance spectroscopy of blood plasma and multivariate pattern recognition techniques. The biochemical response to stress was characterized by decreased levels of total lipoproteins and increased levels of amino acids, glucose, lactate, creatine, and citrate. Secondary acute water avoidance stress also caused elevated levels of O-acetyl glycoproteins in blood plasma. LC-PUFAs dietary enrichment did not alter the metabolic response to stress, but did result in a modified lipoprotein profile. This work indicates that the different stressor types resulted in some common systemic metabolic responses that involve changes in energy and muscle metabolism, but that they are not reversible by dietary intervention.

Angiotensin-converting enzyme inhibitor-related angioedema: how to deal with it

Angiotensin-converting enzyme inhibitor-related angioedema is a well documented condition, which seems to occur in up to 1% of treated patients. It represents a problem for both the clinician and the patient: for the clinician, the diagnosis may be difficult due to its peculiar clinical characteristics, whereas for the misdiagnosed patient the delay prolongs a potentially dangerous situation. If the drug is not discontinued, the attacks tend to become worse and even life-threatening. There are now evidences that increased levels of bradykinin have an important role in the pathophysiology of attacks and, moreover, there are genetic factors that render certain individuals susceptible to angiotensin-converting enzyme inhibitor-related angioedema. In this review, the authors analyse the pathogenetic mechanism, the clinical presentation, the management and future perspectives of research on this condition.

Integrated metabonomic analysis of bromobenzene-induced hepatotoxicity: novel induction of 5-oxoprolinosis

We present here a definitive metabonomic analysis in order to detect novel biomarker and metabolite information, implicating specific putative protein targets in the toxicological mechanism of bromobenzene-induced centrilobular hepatic necrosis. Male Han-Wistar rats were dosed with bromobenzene (1.5 g/kg, n = 25) and blood plasma, urine and liver samples were collected for NMR and magic angle spinning (MAS) NMR spectroscopy at various time-points postdose, with histopathology and clinical pathology performed in parallel. Liver samples were analyzed by 600 MHz 1H MAS NMR techniques and the resultant spectra were correlated to sequential 1H NMR measurements in urine and blood plasma using pattern recognition methods. 1D 1H NMR spectra were data-reduced and analyzed using principal components analysis (PCA) to show the time-dependent biochemical variations induced by bromobenzene toxicity. In addition to a holistic view of the effect of hepatic toxicity on the metabolome, a number of putative protein targets of bromobenzene and its metabolites were identified including those enzymes of the glutathione cycle, exemplified by the presence of a novel biomarker, 5-oxoproline, in liver tissue, blood plasma, and urine. As such, this work establishes the importance of metabonomics technology in resolving the mechanistic complexity of drug toxicity as well as the benefits of frontloading this approach in drug safety evaluation and biomarker discovery.

Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice

Here, we study the intricate relationship between gut microbiota and host cometabolic phenotypes associated with dietary-induced impaired glucose homeostasis and nonalcoholic fatty liver disease (NAFLD) in a mouse strain (129S6) known to be susceptible to these disease traits, using plasma and urine metabotyping, achieved by (1)H NMR spectroscopy. Multivariate statistical modeling of the spectra shows that the genetic predisposition of the 129S6 mouse to impaired glucose homeostasis and NAFLD is associated with disruptions of choline metabolism, i.e., low circulating levels of plasma phosphatidylcholine and high urinary excretion of methylamines (dimethylamine, trimethylamine, and trimethylamine-N-oxide), coprocessed by symbiotic gut microbiota and mammalian enzyme systems. Conversion of choline into methylamines by microbiota in strain 129S6 on a high-fat diet reduces the bioavailability of choline and mimics the effect of choline-deficient diets, causing NAFLD. These data also indicate that gut microbiota may play an active role in the development of insulin resistance.

Metabolic profiling of CSF: evidence that early intervention may impact on disease progression and outcome in schizophrenia

BACKGROUND

The identification of schizophrenia biomarkers is a crucial step towards improving current diagnosis, developing new presymptomatic treatments, identifying high-risk individuals and disease subgroups, and assessing the efficacy of preventative interventions at a rate that is not currently possible.

METHODS AND FINDINGS

(1)H nuclear magnetic resonance spectroscopy in conjunction with computerized pattern recognition analysis were employed to investigate metabolic profiles of a total of 152 cerebrospinal fluid (CSF) samples from drug-naïve or minimally treated patients with first-onset paranoid schizophrenia (referred to as "schizophrenia" in the following text) and healthy controls. Partial least square discriminant analysis showed a highly significant separation of patients with first-onset schizophrenia away from healthy controls. Short-term treatment with antipsychotic medication resulted in a normalization of the disease signature in over half the patients, well before overt clinical improvement. No normalization was observed in patients in which treatment had not been initiated at first presentation, providing the first molecular evidence for the importance of early intervention for psychotic disorders. Furthermore, the alterations identified in drug-naïve patients could be validated in a test sample set achieving a sensitivity and specificity of 82% and 85%, respectively.

CONCLUSIONS

Our findings suggest brain-specific alterations in glucoregulatory processes in the CSF of drug-naïve patients with first-onset schizophrenia, implying that these abnormalities are intrinsic to the disease, rather than a side effect of antipsychotic medication. Short-term treatment with atypical antipsychotic medication resulted in a normalization of the CSF disease signature in half the patients well before a clinical improvement would be expected. Furthermore, our results suggest that the initiation of antipsychotic treatment during a first psychotic episode may influence treatment response and/or outcome.

Fibrinogen and coronary heart disease: test of causality by 'Mendelian randomization'

BACKGROUND

Blood concentrations of fibrinogen have been associated with coronary heart disease risk in epidemiological studies, but it is uncertain whether this association is causal or reflects residual confounding by other risk factors. We investigated the relationship between the single nucleotide polymorphism at position -148 in the beta-fibrinogen gene promoter (beta - 148C/T), blood fibrinogen levels, and risk of myocardial infarction (MI) in sufficiently large numbers of coronary disease cases to reliably address this question.

METHODS

Genotyping and measurement of blood fibrinogen concentration were carried out in 4,685 cases of confirmed MI and 3,460 controls with no history of coronary disease. A meta-analysis of ISIS and 19 other studies of beta-fibrinogen genotypes involving a total of 12,220 coronary disease cases and 18,716 controls was conducted.

RESULTS

Among the ISIS controls, mean plasma fibrinogen concentrations with the C/C, C/T and T/T genotypes were 3.34 (SE 0.015), 3.48 (0.022), and 3.60 (0.064) g/l, respectively, corresponding to an increase of 0.14 (0.024) g/l per T allele (trend P < 0.0001). In the case-control comparison, 0.14 g/l higher usual plasma fibrinogen concentration was associated with an age-adjusted and sex-adjusted risk ratio for MI of 1.17 [95% confidence interval (95% CI) 1.14-1.19; P < 0.0001]. But, after further adjustment for smoking, body mass index, and plasma apolipoprotein B/A(1) ratio, this risk ratio fell to 1.03 (95% CI 1.00-1.05; P = 0.05). Moreover, fibrinogen genotype was not significantly associated with MI incidence: risk ratio of 1.06 (95% CI 0.96-1.16) per higher-fibrinogen allele in ISIS alone and of 1.00 (95% CI 0.95-1.04) per allele in the meta-analysis.

CONCLUSIONS

Genotypes that produce lifelong differences in fibrinogen concentrations do not materially influence coronary disease incidence. As these genotype-dependent differences in fibrinogen were allocated randomly at conception (Mendelian randomization), this association is not likely to be confounded by other factors. Consequently, these genetic results provide strong evidence that long-term differences in fibrinogen concentrations are not a major determinant of coronary disease risk.

A combined 1H-NMR spectroscopy- and mass spectrometry-based metabolomic study of the PPAR-alpha null mutant mouse defines profound systemic changes in metabolism linked to the metabolic syndrome

The mobilization of triacylglycerides from storage in adipocytes to the liver is a vital response to the fasting state in mammalian metabolism. This is accompanied by a rapid translational activation of genes encoding mitochondrial, microsomal, and peroxisomal beta-oxidation in the liver, in part under the regulation of peroxisome proliferator-activated receptor-alpha (PPAR-alpha). A failure to express PPAR-alpha results in profound metabolic perturbations in muscle tissue as well as the liver. These changes represent a number of deficits that accompany diabetes, dyslipidemia, and the metabolic syndrome. In this study, the metabolic role of PPAR-alpha has been investigated in heart, skeletal muscle, liver, and adipose tissue of PPAR-alpha null mice at 1 mo of age using metabolomics. To maximize the coverage of the metabolome in these tissues, (1)H-NMR spectroscopy, magic angle spinning (1)H-NMR spectroscopy, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry were used to examine metabolites in aqueous tissue extracts and intact tissue. The data were analyzed by the multivariate approaches of principal components analysis and partial least squares. Across all tissues, there was a profound decrease in glucose and a number of amino acids, including glutamine and alanine, and an increase in lactate, demonstrating that a failure to express PPAR-alpha results in perturbations in glycolysis, the citric acid cycle, and gluconeogenesis. Furthermore, despite PPAR-alpha being weakly expressed in adipose tissue, a profound metabolic perturbation was detected in this tissue.

The application of NMR-based metabonomics in neurological disorders

Advances in postgenomic technologies have radically changed the information output from complex biological systems, generating vast amounts of high complexity data that can be interpreted by means of chemometric and bioinformatic methods to achieve disease diagnosis and prognosis. High-resolution nuclear magnetic resonance (NMR) spectroscopy of biofluids such as plasma, cerebrospinal fluid (CSF), and urine can generate robust, interpretable metabolic fingerprints that contain latent information relating to physiological or pathological status. This technology has been successfully applied to both preclinical and clinical studies of neurodegenerative diseases such as Huntington's disease, muscular dystrophy, and cerebellar ataxia. An extension of this technology, (1)H magic-angle-spinning (HRMAS) NMR spectroscopy, can be used to generate metabolic information on small intact tissue samples, providing a metabolic link between metabolic profiling of biofluids and histology. In this review we provide a summary of high-resolution NMR studies in neurodegenerative disease and explore the potential of metabonomics in evaluating disease progression with respect to therapeutic intervention.

Statistical Search Space Reduction and Two-Dimensional Data Display Approaches for UPLC−MS in Biomarker Discovery and Pathway Analysis

A new analytical strategy for biomarker recovery from directly coupled ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC Tof MS) data on biofluids is presented and exemplified using a study on hydrazine-induced liver toxicity. A key step in the strategy involves a novel procedure for reducing the spectroscopic search space by differential analysis of cohorts of normal and pathological samples using an orthogonal projection to latent structures discriminant analysis (O-PLS-DA). This efficiently sorts principal discriminators of toxicity from the background of thousands of metabolic features commonly observed in data sets generated by UPLC-MS analysis of biological fluids and is thus a powerful tool for biomarker discovery.

Metabolomic profiling of heat stress: hardening and recovery of homeostasis in Drosophila

Frequent exposure of terrestrial insects to temperature variation has led to the evolution of protective biochemical and physiological mechanisms, such as the heat shock response, which markedly increases the tolerance to heat stress. Insight into such mechanisms has, so far, mainly relied on selective studies of specific compounds or characteristics or studies at the genomic or proteomic levels. In the present study, we have used untargeted NMR metabolomic profiling to examine the biological response to heat stress in Drosophila melanogaster. The metabolite profile was analyzed during recovery after exposure to different thermal stress treatments and compared with untreated controls. Both moderate and severe heat stress gave clear effects on the metabolite profiles. The profiles clearly demonstrated that hardening by moderate heat stress led to a faster reestablishment of metabolite homeostasis after subsequent heat stress. Several metabolites were identified as responsive to heat stress and could be related to known physiological and biochemical responses. The time course of the recovery of metabolite homeostasis mirrored general changes in gene expression, showing that recovery follows the same temporal pattern at these two biological levels. Finally, our data show that heat hardening permits a quicker return to homeostasis, rather than a reduction of the acute metabolic perturbation and that the reestablishment of homeostasis is important for obtaining maximal heat-hardening effect. The results display the power of NMR metabolomic profiling for characterization of the instantaneous physiological condition, enabling direct visualization of the perturbation of and return to homeostasis.

Translating genomic biomarkers into clinically useful diagnostics

The landmark sequencing of the human genome has ushered in a new field of large-scale research. Advances in understanding the molecular basis of disease have opened up new opportunities to develop genomics-based tools to diagnose, predict disease onset or recurrence, tailor treatment options, and assess treatment response. Although still in the early stages of research and development, genomic biomarker research has the capability of providing a comprehensive insight into pathophysiological processes as well as more precise predictors of outcome not previously attainable with traditional biomarkers. Before genomic biomarkers are incorporated into clinical practice, several issues will need to be addressed in order to generate the necessary levels of evidence to demonstrate analytical and clinical validity and utility. In addition, efforts will be needed to educate health professionals and the public about genomics-based tools, revise regulatory oversight mechanisms, and ensure privacy safeguards of the information generated from these new tests.

The Consortium for Metabonomic Toxicology (COMET): aims, activities and achievements

The utility of metabonomics in the evaluation of xenobiotic toxicity has been comprehensively assessed by the Consortium for Metabonomic Toxicology (COMET), formed between five major pharmaceutical companies and Imperial College London, UK. The main objectives were to assess methodologies, to generate a metabonomic database using (1)H nuclear magnetic resonance (NMR) spectroscopy of rodent urine and blood serum and to build a predictive expert system for target organ toxicity. The analytic and biologic variation that might arise through the use of metabonomics was evaluated and a high degree of robustness demonstrated. With the completion of 147 studies, the chief deliverables of a curated database of rodent biofluid NMR spectra and computer-based expert systems for the prediction of kidney or liver toxicity in rat and mouse based on the spectral data have been generated, and delivered to the sponsoring companies. The project, with its relatively modest resources, has met and exceeded all of its targets and was judged a resounding success by the sponsoring companies who are, in many cases, already enhancing and making use of the data in their in-house studies.