1H NMR-based metabonomics for the diagnosis of inborn errors of metabolism in urine

The Effect of Blood Contained in the Samples on the Metabolomic Profile of Mouse Brain Tissue: A Study by NMR Spectroscopy

(1) Recently, metabolic profiling of the tissue in the native state or extracts of its metabolites has become increasingly important in the field of metabolomics. An important factor, in this case, is the presence of blood in a tissue sample, which can potentially lead to a change in the concentration of tissue metabolites and, as a result, distortion of experimental data and their interpretation. (2) In this paper, the metabolomic profiling based on NMR spectroscopy was performed to determine the effect of blood contained in the studied samples of brain tissue on their metabolomic profile. We used 13 male laboratory CD-1^® IGS mice for this study. The animals were divided into two groups. The first group of animals (n = 7) was subjected to the perfusion procedure, and the second group of animals (n = 6) was not perfused. The brain tissues of the animals were homogenized, and the metabolite fraction was extracted with a water/methanol/chloroform solution. Samples were studied by high-frequency ¹H-NMR spectroscopy with subsequent statistical data analysis. The group comparison was performed with the use of the Student's test. We identified 36 metabolites in the brain tissue with the use of NMR spectroscopy. (3) For the major set of studied metabolites, no significant differences were found in the brain tissue metabolite concentrations in the native state and after the blood removal procedure. (4) Thus, it was shown that the presence of blood does not have a significant effect on the metabolomic profile of the brain in animals without pathologies.

Addressing the standardisation of internal standards and preservative used in human bio fluids for NMR analysis: a method optimization

OBJECTIVES

A database comprising multivariate data in developing a model from nuclear magnetic resonance (NMR) analysis using human bio fluids are necessary to have reproducibility and reliability of the data. To achieve reproducibility of the data, standardised experiments, including internal standard and preservative used should be attained, especially for samples such as human bio fluids to hinder the variation among samples. The aim of the study was to optimise in commonly used human bio fluids (serum and urine) for a suitable internal standard and preservative used in extended storage samples for NMR analysis.

METHODS

Serum and urine samples were collected from healthy human subjects. The experiment was divided into two parts, part one to evaluate 2,2,2,2-tetradeutero-4,4-dimethyl-4-silapentanoic acid (TSP) and 4,4-dimethyl-4-silapentane-1-ammonium trifluoroacetate (DSA) as the optimal internal standard for the serum and urine samples. The second part investigated the effects of preservatives in the serum and urine samples on extended storage.

RESULTS

Overall, TSP and DSA are suitable to be used as an internal standard in human urine samples. However, DSA is a superior internal standard in serum samples for NMR analysis. For the effect of preservative, the results indicated that human serum and urine samples could be stored without addition of preservative in -80 °C, as no changes in NMR fingerprinting have been observed during storage in the absence or presence of the preservative.

CONCLUSIONS

The findings suggest the use of DSA and TSP as an internal standard in serum and urine samples, respectively. Storage of serum and urine samples without any addition of preservative for an extended period has no effect on the metabolites changes. By having a standardised method, it will offer a considerable saving in both operator and spectrometer time and most importantly produce reproducible and reliable data.

Addressing the standardisation of internal standards and preservative used in human bio fluids for NMR analysis: a method optimization

OBJECTIVES

A database comprising multivariate data in developing a model from nuclear magnetic resonance (NMR) analysis using human bio fluids are necessary to have reproducibility and reliability of the data. To achieve reproducibility of the data, standardised experiments, including internal standard and preservative used should be attained, especially for samples such as human bio fluids to hinder the variation among samples. The aim of the study was to optimise in commonly used human bio fluids (serum and urine) for a suitable internal standard and preservative used in extended storage samples for NMR analysis.

METHODS

Serum and urine samples were collected from healthy human subjects. The experiment was divided into two parts, part one to evaluate 2,2,2,2-tetradeutero-4,4-dimethyl-4-silapentanoic acid (TSP) and 4,4-dimethyl-4-silapentane-1-ammonium trifluoroacetate (DSA) as the optimal internal standard for the serum and urine samples. The second part investigated the effects of preservatives in the serum and urine samples on extended storage.

RESULTS

Overall, TSP and DSA are suitable to be used as an internal standard in human urine samples. However, DSA is a superior internal standard in serum samples for NMR analysis. For the effect of preservative, the results indicated that human serum and urine samples could be stored without addition of preservative in -80 °C, as no changes in NMR fingerprinting have been observed during storage in the absence or presence of the preservative.

CONCLUSIONS

The findings suggest the use of DSA and TSP as an internal standard in serum and urine samples, respectively. Storage of serum and urine samples without any addition of preservative for an extended period has no effect on the metabolites changes. By having a standardised method, it will offer a considerable saving in both operator and spectrometer time and most importantly produce reproducible and reliable data.

The Presence of Caffeic Acid in Cerebrospinal Fluid: Evidence That Dietary Polyphenols Can Cross the Blood-Brain Barrier in Humans

Epidemiological data indicate that a diet rich in plant polyphenols has a positive effect on brain functions, improving memory and cognition in humans. Direct activity of ingested phenolics on brain neurons may be one of plausible mechanisms explaining these data. This also suggests that some phenolics can cross the blood-brain barrier and be present in the brain or cerebrospinal fluid. We measured 12 phenolics (a combination of the solid-phase extraction technique with high-performance liquid chromatography) in cerebrospinal fluid and matched plasma samples from 28 patients undergoing diagnostic lumbar puncture due to neurological disorders. Homovanillic acid, 3-hydroxyphenyl acetic acid and caffeic acid were detectable in cerebrospinal fluid reaching concentrations (median; interquartile range) 0.18; 0.14 µmol/L, 4.35; 7.36 µmol/L and 0.02; 0.01 µmol/L, respectively. Plasma concentrations of caffeic acid (0.03; 0.01 µmol/L) did not correlate with those in cerebrospinal fluid (ρ = −0.109, p = 0.58). Because food (fruits and vegetables) is the only source of caffeic acid in human body fluids, our results indicate that the same dietary phenolics can cross blood-brain barrier in humans, and that transportation of caffeic acid through this barrier is not the result of simple or facilitated diffusion.

Psychopharmacological effects of riparin III from Aniba riparia (Nees) Mez. (Lauraceae) supported by metabolic approach and multivariate data analysis

Background

Currently there is a high prevalence of humor disorders such as anxiety and depression throughout the world, especially concerning advanced age patients. Aniba riparia (Nees) Mez. (Lauraceae), popular known as “louro”, can be found from the Amazon through Guianas until the Andes. Previous studies have already reported the isolation of alkamide-type alkaloids such as riparin III (O-methyl-N-2,6-dyhydroxy-benzoyl tyramine) which has demonstrated anxiolytic and antidepressant-like effects in high doses by intraperitoneal administration.

Methods

Experimental protocol was conducted in order to analyze the anxiolytic-like effect of riparin III at lower doses by intravenous administration to Wistar rats (Rattus norvegicus) (n = 5). The experimental approach was designed to last 15 days, divided in 3 distinct periods of five days: control, anxiogenic and treatment periods. The anxiolytic-like effect was evaluated by experimental behavior tests such as open field and elevated plus-maze test, combined with urine metabolic footprint analysis. The urine was collected daily and analyzed by ¹H NMR. Generated data were statistically treated by Principal Component Analysis in order to detect patterns among the distinct periods evaluated as well as biomarkers responsible for its distinction.

Results

It was observed on treatment group that cortisol, biomarker related to physiological stress was reduced, indicating anxiolytic-like effect of riparin III, probably through activation of 5-HT_2A receptors, which was corroborated by behavioral tests.

Conclusion

¹H NMR urine metabolic footprint combined with multivariate data analysis have demonstrated to be an important diagnostic tool to prove the anxiolytic-like effect of riparin III in a more efficient and pragmatic way.

Metabolic changes in early neonatal life: NMR analysis of the neonatal metabolic profile to monitor postnatal metabolic adaptations

BACKGROUND

A major challenge from the moment a child is delivered is the adaptation to the extrauterine life, where rapid metabolic changes take place. The study of these changes during the first days of human life may assist in the understanding of the metabolic processes that occur at this critical period, which is likely to provide significant clinical insights. To date, metabolomics has become a powerful field, ideal for the monitoring of such dynamic variations, since it offers the possibility to identify alterations in metabolic profiles, even on daily basis.

METHODS

The study included 253 healthy newborns (GA 35 to 40 weeks) from the region of Western Greece. Urine samples were collected immediately after birth and at the third day of life. NMR-based metabolomics was used to compare the metabolic urinary profiles of newborns from the first and third day of their life, assessing the impact of six perinatal factors; delivery mode, prematurity, maternal smoking, gender, nutrition and neonatal jaundice.

RESULTS

Analysis of urine metabolic fingerprint from the first and third day of life, coupled with multivariate statistics, provides insights into the details of early life metabolic profile differentiation. Αt the third day of life metabolic adaptations are evident, as many differences were noted in urine of healthy neonates within the first 72 h postpartum. Trends in differentiation of metabolites levels between the two groups, late preterm and term newborns, have been also observed.

CONCLUSIONS

Newborn's urine metabolic profiles confirmed the rapid changes in their metabolism after birth. Further, ongoing research will enable us to develop one reference model of urinary metabolomics in healthy newborns during the period of adaptation to the extra-uterine life.

NMR-based newborn urine screening for optimized detection of inherited errors of metabolism

Inborn errors of metabolism (IEMs) are rare diseases produced by the accumulation of abnormal amounts of metabolites, toxic to the newborn. When not detected on time, they can lead to irreversible physiological and psychological sequels or even demise. Metabolomics has emerged as an efficient and powerful tool for IEM detection in newborns, children, and adults with late onset. In here, we screened urine samples from a large set of neonates (470 individuals) from a homogeneous population (Basque Country), for the identification of congenital metabolic diseases using NMR spectroscopy. Absolute quantification allowed to derive a probability function for up to 66 metabolites that adequately describes their normal concentration ranges in newborns from the Basque Country. The absence of another 84 metabolites, considered abnormal, was routinely verified in the healthy newborn population and confirmed for all but 2 samples, of which one showed toxic concentrations of metabolites associated to ketosis and the other one a high trimethylamine concentration that strongly suggested an episode of trimethylaminuria. Thus, a non-invasive and readily accessible urine sample contains enough information to assess the potential existence of a substantial number (>70) of IEMs in newborns, using a single, automated and standardized ¹H- NMR-based analysis.

Metabolites Re-programming and Physiological Changes Induced in Scenedesmus regularis under Nitrate Treatment

Nitrate is required to maintain the growth and metabolism of plant and animals. Nevertheless, in excess amount such as polluted water, its concentration can be harmful to living organisms such as microalgae. Recently, studies on microalgae response towards nutrient fluctuation are usually limited to lipid accumulation for the production of biofuels, disregarding the other potential of microalgae to be used in wastewater treatments and as source of important metabolites. Our study therefore captures the need to investigate overall metabolite changes via NMR spectroscopy approach coupled with multivariate data to understand the complex molecular process under high (4X) and low (1/4X) concentrations of nitrate (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{NO}}}_{{\bf{3}}}^{{\boldsymbol{-}}}$$\end{document}NO3−). NMR spectra with the aid of chemometric analysis revealed contrasting metabolites makeup under abundance and limited nitrate treatment. By using NMR technique, 43 types of metabolites and 8 types of fatty acid chains were detected. Nevertheless, only 20 key changes were observed and 16 were down regulated in limited nitrate condition. This paper has demonstrated the feasibility of NMR-based metabolomics approach to study the physiological impact of changing environment such as pollution to the implications for growth and productivity of microalgae population.

Biofluid Metabolomics in Preterm Birth Research

This article presents an account of the research carried out so far in the use of metabolomics to find biomarkers of preterm birth (PTB) in fetal, maternal, and newborn biofluids. Metabolomic studies have employed mainly nuclear magnetic resonance spectroscopy or mass spectrometry-based methodologies to analyze, on one hand, prenatal biofluids (amniotic fluid, maternal urine/maternal blood, cervicovaginal fluid) to identify predictive biomarkers of PTB, and on the other hand, biofluids collected at or after birth (amniotic fluid, umbilical cord blood, newborn urine, and newborn blood, maternal blood, or breast milk) to assess and follow up the health status of PTB babies. Besides advancing on the biochemical knowledge of PTB metabolism mainly during the in utero period and at birth, the work carried out has also helped to identify important requirements related to experimental design and analytical protocol that need to be addressed, if translation of these biomarkers to the clinic is to be envisaged. An outlook of possible future developments for the translation of laboratory results to the clinic is presented.

Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics

Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.

Metabonomic profiles reveal dose-dependent effects of Bu-Shen-Gu-Chi-Wan on the serum in experimental periodontitis of rat model

ETHNO-PHARMACOLOGICAL RELEVANCE

Bu-Shen-Gu-Chi-Wan is a Chinese patent medicine for the treatment of chronic periodontitis. It has an anti-inflammatory function in experimental periodontitis and can improve alveolar bone remodeling.

AIM OF THE STUDY

This study aims to explore the effects of Bu-Shen-Gu-Chi-Wan on serum metabolism in a rat model of periodontitis using ¹H nuclear magnetic resonance (¹H NMR) based metabonomics.

MATERIALS AND METHODS

The model of experimental periodontitis in a rat was established by steel wire ligation, plus a high glucose diet and Porphyromonas gingivalis inoculation. When the models had been established, 6-week-old Sprague-Dawley female rats (n=31) were divided into 5 groups: high dose group (Group H), medium dose group (Group M), low dose group (Group L), periodontitis group (Group P) and healthy control group (Group N). Rats in Group H, M and L were given the Bu-Shen-Gu-Chi-Wan solution (0.8, 2 and 4g/kg of body weight) daily for 60 days. Rats in Group P and N were administered normal saline (10ml/kg of body weight) in the same period. All rats were sacrificed at the end of the study and serum samples were collected. The metabolites in the serum were analyzed using ¹H NMR in conjunction with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

RESULTS

The metabonomic profiles of five groups showed the clustering of individual dataset in every group, and the 1st principal component and the 2nd principal component could significantly differentiate the datasets of medium dose or high dose group from other groups. The chemical shift regions at δ 1.22ppm, 1.86ppm, 2.26ppm, 2.34ppm and 2.42ppm showed the most obvious differences among the five groups. The correspondent metabolites were high density lipoprotein, pyruvate/oxaloacetate, arginine and glutamine.

CONCLUSION

The effects of Bu-Shen-Gu-Chi-Wan on the rat serum metabolites were dose dependent. High density lipoprotein, pyruvate/oxaloacetate, arginine and glutamine may be the serum biomarkers of Bu-Shen-Gu-Chi-Wan.

Optimization of cell disruption methods for efficient recovery of bioactive metabolites via NMR of three freshwater microalgae (chlorophyta)

This study demonstrates the use of NMR techniques coupled with chemometric analysis as a high throughput data mining method to identify and examine the efficiency of different disruption techniques tested on microalgae (Chlorella variabilis, Scenedesmus regularis and Ankistrodesmus gracilis). The yield and chemical diversity from the disruptions together with the effects of pre-oven and pre-freeze drying prior to disruption techniques were discussed. HCl extraction showed the highest recovery of oil compounds from the disrupted microalgae (up to 90%). In contrast, NMR analysis showed the highest intensity of bioactive metabolites obtained for homogenized extracts pre-treated with freeze-drying, indicating that homogenizing is a more favorable approach to recover bioactive substances from the disrupted microalgae. The results show the potential of NMR as a useful metabolic fingerprinting tool for assessing compound diversity in complex microalgae extracts.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

1H NMR Metabolomics Study of Metastatic Melanoma in C57BL/6J Mouse Spleen

Melanoma is a malignant tumor of melanocytes. Although extensive investigations have been done to study metabolic changes in primary melanoma in vivo and in vitro, little effort has been devoted to metabolic profiling of metastatic tumors in organs other than lymph nodes. In this work, NMR-based metabolomics combined with multivariate data analysis is used to study metastatic B16-F10 melanoma in C57BL/6J mouse spleen. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to find important metabolites responsible for discriminating the control and the melanoma groups. Two different strategies, i.e. spectral binning and spectral deconvolution, are used to reduce the original spectral data before statistical analysis. Spectral deconvolution is found to be superior for identifying a set of discriminatory metabolites between the control and the melanoma groups, especially when the sample size is small. OPLS results show that the melanoma group can be well separated from its control group. It is found that taurine, glutamate, aspartate, O-Phosphoethanolamine, niacinamide,ATP, lipids and glycerol derivatives are decreased statistically and significantly while alanine, malate, xanthine, histamine, dCTP, GTP, thymidine, 2'-Deoxyguanosine are statistically and significantly elevated. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in spleen.

Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum

Focused metabolic profiling is a powerful tool for the determination of biomarkers. Here, a more global proton nuclear magnetic resonance ((1)H NMR)-based metabolomic approach coupled with a relative simple ultra high performance liquid chromatography (UHPLC)-based focused metabolomic approach was developed and compared to characterize the systemic metabolic disturbances underlying esophageal cancer (EC) and identify possible early biomarkers for clinical prognosis. Serum metabolic profiling of patients with EC (n=25) and healthy controls (n=25) was performed by using both (1)H NMR and UHPLC, and metabolite identification was achieved by multivariate statistical analysis. Using orthogonal projection to least squares discriminant analysis (OPLS-DA), we could distinguish EC patients from healthy controls. The predictive power of the model derived from the UHPLC-based focused metabolomics performed better in both sensitivity and specificity than the results from the NMR-based metabolomics, suggesting that the focused metabolomic technique may be of advantage in the future for the determination of biomarkers. Moreover, focused metabolic profiling is highly simple, accurate and specific, and should prove equally valuable in metabolomic research applications. A total of nineteen significantly altered metabolites were identified as the potential disease associated biomarkers. Significant changes in lipid metabolism, amino acid metabolism, glycolysis, ketogenesis, tricarboxylic acid (TCA) cycle and energy metabolism were observed in EC patients compared with the healthy controls. These results demonstrated that metabolic profiling of serum could be useful as a screening tool for early EC diagnosis and prognosis, and might enhance our understanding of the mechanisms involved in the tumor progression.

Urine metabonomic profiling of a female adolescent with PIT-1 mutation before and during growth hormone therapy: insights into the metabolic effects of growth hormone

OBJECTIVE

Growth hormone (GH) is a protein hormone with important roles in growth and metabolism. The objective of this study was to investigate the metabolism of a human subject with severe GH deficiency (GHD) due to a PIT-1 gene mutation and the metabolic effects of GH therapy using Nuclear Magnetic Resonance (NMR)-based metabonomics. NMR-based metabonomics is a platform that allows the metabolic profile of biological fluids such as urine to be recorded, and any alterations in the profile modulated by GH can potentially be detected.

DESIGN

Urine samples were collected from a female subject with severe GHD before, during and after GH therapy, and from healthy age- and sex-matched controls and analysed with NMR-based metabonomics.

SETTING

The samples were collected at a hospital and the study was performed at a research facility.

PARTICIPANTS

We studied a 17 year old female adolescent with severe GHD secondary to PIT-1 gene mutation who had reached final adult height and who had ceased GH therapy for over 3 years. The subject was subsequently followed for 5 years with and without GH therapy. Twelve healthy age-matched female subjects acted as control subjects.

INTERVENTION

The GH-deficient subject re-commenced GH therapy at a dose of 1 mg/day to normalise serum IGF-1 levels.

MAIN OUTCOME MEASURES

Urine metabolic profiles were recorded using NMR spectroscopy and analysed with multivariate statistics to distinguish the profiles at different time points and identify significant metabolites affected by GH therapy.

RESULTS

NMR-based metabonomics revealed that the metabolic profile of the GH-deficient subject altered with GH therapy and that her profile was different from healthy controls before, and during withdrawal of GH therapy.

CONCLUSION

This study illustrates the potential use of NMR-based metabonomics for monitoring the effects of GH therapy on metabolism by profiling the urine of GH-deficient subjects. Further controlled studies in larger numbers of GH-deficient subjects are required to determine the clinical benefits of NMR-based metabonomics in subjects receiving GH therapy.

Metabonomics-based study of clinical urine samples in suboptimal health with different syndromes

Objective. To explore the urinary biochemistry features of syndromes of traditional Chinese medicine (TCM) such as syndrome of stagnation of liver Qi, spleen deficiency, liver Qi stagnation, and spleen deficiency (LSSDS) in sub-optimal health status (SHS). Methods. 12 cases for each syndrome group in SHS were selected, 12 subjects were used as a normal control group, and ¹H NMR detection was, respectively, carried out, and the data was corrected by the orthogonal signal correction (OSC) and then adopted a partial least squares (PLS) method for discriminate analysis. Results. The OSC-PLS (ctr) analysis results of the nuclear overhauser enhancement spectroscopy (NOESY) detection indicated that the syndromes in SHS could be differentiated, and there were significant differences in the levels of metabolites of the urine samples of the four groups; the biomarkers of LSSDS in SHS were found out. The contents of citric acid (2.54 and 2.66), trimethylamineoxide (3.26), and hippuric acid (3.98, 7.54, 7.58, 7.62, 7.66, 7.82, and 7.86) in the urine samples of LSSDS group were lower than that of the normal control group. Conclusion. There are differences in the ¹H-NMR metabolic spectrum of the urine samples of the four groups, and the specific metabolic products of the LSSDS in SHS can be identified from metabonomics analysis.

Metabolic changes in urine during and after pregnancy in a large, multiethnic population-based cohort study of gestational diabetes

This study aims to identify novel markers for gestational diabetes (GDM) in the biochemical profile of maternal urine using NMR metabolomics. It also catalogs the general effects of pregnancy and delivery on the urine profile. Urine samples were collected at three time points (visit V1: gestational week 8-20; V2: week 28±2; V3 10-16 weeks post partum) from participants in the STORK Groruddalen program, a prospective, multiethnic cohort study of 823 healthy, pregnant women in Oslo, Norway, and analyzed using (1)H-NMR spectroscopy. Metabolites were identified and quantified where possible. PCA, PLS-DA and univariate statistics were applied and found substantial differences between the time points, dominated by a steady increase of urinary lactose concentrations, and an increase during pregnancy and subsequent dramatic reduction of several unidentified NMR signals between 0.5 and 1.1 ppm. Multivariate methods could not reliably identify GDM cases based on the WHO or graded criteria based on IADPSG definitions, indicating that the pattern of urinary metabolites above micromolar concentrations is not influenced strongly and consistently enough by the disease. However, univariate analysis suggests elevated mean citrate concentrations with increasing hyperglycemia. Multivariate classification with respect to ethnic background produced weak but statistically significant models. These results suggest that although NMR-based metabolomics can monitor changes in the urinary excretion profile of pregnant women, it may not be a prudent choice for the study of GDM.

Interpretation and visualization of non-linear data fusion in kernel space: study on metabolomic characterization of progression of multiple sclerosis

Background

In the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required.

Methodology

The main aim is to demonstrate a novel approach for data fusion for classification; the approach is applied to metabolomics datasets coming from patients suffering from MScl at a different stage of the disease. The approach involves data fusion in kernel space and consists of four main steps. The first one is to extract the significant information per data source using Support Vector Machine Recursive Feature Elimination. This method allows one to select a set of relevant variables. In the next step the optimized kernel matrices are merged by linear combination. In step 3 the merged datasets are analyzed with a classification technique, namely Kernel Partial Least Square Discriminant Analysis. In the final step, the variables in kernel space are visualized and their significance established.

Conclusions

We find that fusion in kernel space allows for efficient and reliable discrimination of classes (MScl and early stage). This data fusion approach achieves better class prediction accuracy than analysis of individual datasets and the commonly used mid-level fusion. The prediction accuracy on an independent test set (8 samples) reaches 100%. Additionally, the classification model obtained on fused kernels is simpler in terms of complexity, i.e. just one latent variable was sufficient. Finally, visualization of variables importance in kernel space was achieved.

Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations

Increasingly sophisticated measurement technologies have allowed the fields of metabolomics and genomics to identify, in parallel, risk factors of disease; predict drug metabolism; and study metabolic and genetic diversity in large human populations. Yet the complementarity of these fields and the utility of studying genes and metabolites together is belied by the frequent separate, parallel applications of genomic and metabolomic analysis. Early attempts at identifying co-variation and interaction between genetic variants and downstream metabolic changes, including metabolic profiling of human Mendelian diseases and quantitative trait locus mapping of individual metabolite concentrations, have recently been extended by new experimental designs that search for a large number of gene-metabolite associations. These approaches, including metabolomic quantitiative trait locus mapping and metabolomic genome-wide association studies, involve the concurrent collection of both genomic and metabolomic data and a subsequent search for statistical associations between genetic polymorphisms and metabolite concentrations across a broad range of genes and metabolites. These new data-fusion techniques will have important consequences in functional genomics, microbial metagenomics and disease modeling, the early results and implications of which are reviewed.

IQMNMR: Open source software using time-domain NMR data for automated identification and quantification of metabolites in batches

BACKGROUND

One of the most promising aspects of metabolomics is metabolic modeling and simulation. Central to such applications is automated high-throughput identification and quantification of metabolites. NMR spectroscopy is a reproducible, nondestructive, and nonselective method that has served as the foundation of metabolomics studies. However, the automated high-throughput identification and quantification of metabolites in NMR spectroscopy is limited by severe spectral overlap. Although numerous software programs have been developed for resolving overlapping resonances, as well as for identifying and quantifying metabolites, most of these programs are frequency-domain methods, considerably influenced by phase shifts and baseline distortions, and effective only in small-scale studies. Almost all these programs require multiple spectra for each application, and do not automatically identify and quantify metabolites in batches.

RESULTS

We created IQMNMR, an R package that integrates a relaxation algorithm, digital filter, and similarity search algorithm. It differs from existing software in that it is a time-domain method; it uses not only frequency to resolve overlapping resonances but also relaxation time constants; it requires only one NMR spectrum per application; is uninfluenced by phase shifts and baseline distortions; and most important, yields a batch of quantified metabolites.

CONCLUSIONS

IQMNMR provides a solution that can automatically identify and quantify metabolites by one-dimensional proton NMR spectroscopy. Its time-domain nature, stability against phase shifts and baseline distortions, requirement for only one NMR spectrum, and capability to output a batch of quantified metabolites are of considerable significance to metabolic modeling and simulation.IQMNMR is available at http://cran.r-project.org/web/packages/IQMNMR/.

Advances in NMR-based biofluid analysis and metabolite profiling

Significant improvements in NMR technology and methods have propelled NMR studies to play an important role in a rapidly expanding number of applications involving the profiling of metabolites in biofluids. This review discusses recent technical advances in NMR spectroscopy based metabolite profiling methods, data processing and analysis over the last three years.

An optimised sample preparation method for NMR-based faecal metabonomic analysis

Faecal metabonomic NMR analysis plays an essential role in investigating the interactions between mammalian metabolism and symbiotic gut microbiota. However, the faecal metabolite extraction method remains to be optimised and standardised to take into consideration signal-to-noise ratios, pH and chemical shift consistency. In the current investigation, we compared extraction consistency of three homogenisation methods including manual ultrasonication, automatic homogenization with tissuelyser and their combination, and systematically optimised faecal metabolite extraction parameters, including the faeces-to-buffer ratio (W(f) : V(b)), extraction repetition times and duration. We found that automatic homogenisation with tissuelyser was the choice of extraction method owning to its good metabolite extraction consistency and high throughput. We also recommend W(f) : V(b) of 1 : 10 (mg microl(-1)) and use of the combined first two extracts as the resultant samples to represent faecal metabolite composition. Such recommendation is based on considerations of maximisation of the spectral signal-to-noise ratio, pH and chemical shift consistency, completeness of metabolite extraction and sample preparation throughput so that the method is suitable for analysing a large number of samples especially in human population studies.

Chemometrics in metabolomics--a review in human disease diagnosis

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

1H NMR metabolomics study of age profiling in children

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

An exploratory NMR nutri-metabonomic investigation reveals dimethyl sulfone as a dietary biomarker for onion intake

The metabolome following intake of onion by-products is evaluated. Thirty-two rats were fed a diet containing an onion by-product or one of the two derived onion by-product fractions: an ethanol extract and the residue. A 24 hour urine sample was analyzed using (1)H NMR spectroscopy in order to investigate the effects of onion intake on the rat metabolism. Application of interval extended canonical variates analysis (ECVA) proved to be able to distinguish between the metabolomic profiles from rats consuming normal feed and rats fed with an onion diet. Two dietary biomarkers for onion intake were identified as dimethyl sulfone and 3-hydroxyphenylacetic acid. The same two dietary biomarkers were subsequently revealed by interval partial least squares regression (PLS) to be perfect quantitative markers for onion intake. The best PLS calibration model yielded a root mean square error of cross-validation (RMSECV) of 0.97% (w/w) with only 1 latent variable and a squared correlation coefficient of 0.94. This indicates that urine from rats on the by-product diet, the extract diet, and the residue diet all contain the same dietary biomarkers and it is concluded that dimethyl sulfone and 3-hydroxyphenylacetic acid are dietary biomarkers for onion intake. Being able to detect specific dietary biomarkers is highly beneficial in the control of nutritionally enhanced functional foods.

Characterization of inflammatory bowel disease with urinary metabolic profiling

OBJECTIVES

Distinguishing between the inflammatory bowel disease (IBD), Crohn's disease (CD), and ulcerative colitis (UC) is important for both management and prognostic reasons. Discrimination using noninvasive techniques could be an adjunct to conventional diagnostics. Differences have been shown between the intestinal microbiota of CD and UC patients and controls; the gut bacteria influence specific urinary metabolites that are quantifiable using proton high-resolution nuclear magnetic resonance (NMR) spectroscopy. This study tested the hypothesis that such metabolites differ between IBD and control cohorts, and that using multivariate pattern-recognition analysis, the cohorts could be distinguished by urine NMR spectroscopy.

METHODS

NMR spectra were acquired from urine samples of 206 Caucasian subjects (86 CD patients, 60 UC patients, and 60 healthy controls). Longitudinal samples were collected from 75 individuals. NMR resonances specific for metabolites influenced by the gut microbes were studied, including hippurate, formate, and 4-cresol sulfate. Multivariate analysis of all urinary metabolites involved principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

RESULTS

Hippurate levels were lowest in CD patients and differed significantly between the three cohorts (P<0.0001). Formate levels were higher and 4-cresol sulfate levels lower in CD patients than in UC patients or controls (P=0.0005 and P=0.0002, respectively). PCA revealed clustering of the groups; PLS-DA modeling was able to distinguish the cohorts. These results were independent of medication and diet and were reproducible in the longitudinal cohort.

CONCLUSIONS

Specific urinary metabolites related to gut microbial metabolism differ between CD patients, UC patients, and controls. The emerging technique of urinary metabolic profiling with multivariate analysis was able to distinguish these cohorts.

An optimized buffer system for NMR-based urinary metabonomics with effective pH control, chemical shift consistency and dilution minimization

NMR-based metabonomics has been widely employed to understand the stressor-induced perturbations to mammalian metabolism. However, inter-sample chemical shift variations for metabolites remain an outstanding problem for effective data mining. In this work, we systematically investigated the effects of pH and ionic strength on the chemical shifts for a mixture of 9 urinary metabolites. We found that the chemical shifts were decreased with the rise of pH but increased with the increase of ionic strength, which probably resulted from the pH- and ionic strength-induced alteration to the ionization equilibrium for the function groups. We also found that the chemical shift variations for most metabolites were reduced to less than 0.004 ppm when the pH was 7.1-7.7 and the salt concentration was less than 0.15 M. Based on subsequent optimization to minimize chemical shift variation, sample dilution and maximize the signal-to-noise ratio, we proposed a new buffer system consisting of K(2)HPO(4) and NaH(2)PO(4) (pH 7.4, 1.5 M) with buffer-urine volume ratio of 1 : 10 for human urinary metabonomic studies; we suggest that the chemical shifts for the proton signals of citrate and aromatic signals of histidine be corrected prior to multivariate data analysis especially when high resolution data were employed. Based on these, an optimized sample preparation method has been developed for NMR-based urinary metabonomic studies.

Metabolomics-based methods for early disease diagnostics

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.

Global metabolic profiling and its role in systems biology to advance personalized medicine in the 21st century

Systems biology attempts to elucidate the complex interaction between genes, proteins and metabolites to provide a mechanistic understanding of cellular function and how this function is affected by disease processes, drug toxicity or drug efficacy effects. Global metabolic profiling is an important component of systems biology that can be applied in both preclinical and clinical settings for drug discovery and development, and to study disease mechanisms. The metabolic profile encodes the phenotype, which is composed of the genotype and environmental factors. The phenotypic profile can be used to make decisions about the best course of treatment for an individual patient. Understanding the combined effects of genetics and environment through a systems biology framework will enable the advancement of personalized medicine.

Multilevel data analysis of a crossover designed human nutritional intervention study

A new method is introduced for the analysis of 'omics' data derived from crossover designed drug or nutritional intervention studies. The method aims at finding systematic variations in metabolic profiles after a drug or nutritional challenge and takes advantage of the crossover design in the data. The method, which can be considered as a multivariate extension of a paired t test, generates different multivariate submodels for the between- and the within-subject variation in the data. A major advantage of this variation splitting is that each submodel can be analyzed separately without being confounded with the other variation sources. The power of the multilevel approach is demonstrated in a human nutritional intervention study which used NMR-based metabolomics to assess the metabolic impact of grape/wine extract consumption. The variations in the urine metabolic profiles are studied between and within the human subjects using the multilevel analysis. After variation splitting, multilevel PCA is used to investigate the experimental and biological differences between the subjects, whereas a multilevel PLS-DA model is used to reveal the net treatment effect within the subjects. The observed treatment effect is validated with cross model validation and permutations. It is shown that the statistical significance of the multilevel classification model ( p << 0.0002) is a major improvement compared to a ordinary PLS-DA model ( p = 0.058) without variation splitting. Finally, rank products are used to determine which NMR signals are most important in the multilevel classification model.

Correlative and quantitative 1H NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats

Type 1 diabetes was induced in Sprague-Dawley rats using streptozotocin. Rat urine samples (8 diabetic and 10 control) were analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy. The derived metabolites using univariate and multivariate statistical analysis were subjected to correlative analysis. Plasma metabolites were measured by a series of bioassays. A total of 17 urinary metabolites were identified in the 1H NMR spectra and the loadings plots after principal components analysis. Diabetic rats showed significantly increased levels of glucose (P < 0.00001), alanine (P < 0.0002), lactate (P < 0.05), ethanol (P < 0.05), acetate (P < 0.05), and fumarate (P < 0.05) compared with controls. Plasma assays showed higher amounts of glucose, urea, triglycerides, and thiobarbituric acid-reacting substances in diabetic rats. Striking differences in the Pearson's correlation of the 17 NMR-detected metabolites were observed between control and diabetic rats. Detailed analysis of the altered metabolite levels and their correlations indicate a significant disturbance in the glucose metabolism and tricarboxylic acid (TCA) cycle and a contribution from gut microbial metabolism. Specific perturbed metabolic pathways include the glucose-alanine and Cori cycles, the acetate switch, and choline metabolism. Detection of the altered metabolic pathways and bacterial metabolites using this correlative and quantitative NMR-based metabolomics approach should help to further the understanding of diabetes-related mechanisms.

Hydrophilic interaction chromatography for mass spectrometric metabonomic studies of urine

High-performance liquid chromatography (LC) coupled to mass spectrometry (MS) is increasingly being used for urinary metabonomic studies. Most studies utilize reversed-phase separation techniques, which are not suited to retaining highly polar analytes. Metabonomic studies should encompass a representative "fingerprint" that contains the largest amount of information possible. In this work, we have analyzed human urine samples with LC-MS, comparing traditional reversed-phase separation with hydrophilic interaction chromatography (HILIC), using both positive and negative electrospray ionization modes. The resulting data were analyzed using principal components analysis and partial least-squares-discriminant analysis. Discriminant models were developed for the response variables gender, diurnal variation, and age and were evaluated using external test sets to classify their predictive ability. The developed models using both positive and negative ionization mode data for reversed-phase and HILIC separations were very comparable, indicating that HILIC is a suitable method for increasing the fingerprint coverage for LC-MS metabonomic studies.

Effect of magnetic field strength on NMR-based metabonomic human urine data. Comparative study of 250, 400, 500, and 800 MHz

Metabonomic analysis of urine utilizing high-resolution NMR spectroscopy and chemometric techniques has proven valuable in characterizing the biochemical response to an intervention. To assess the effect of magnetic field strength on information contained in NMR-based metabonomic data sets, 1H NMR spectra were acquired on 250-, 400-, 500-, and 800-MHz instruments, respectively, on the same set of human urine samples collected before and after dietary interventions with milk and with meat proteins. Partial least-squares regression discriminant analyses (PLS-DA) were performed in order to elucidate the ability of the 1H spectra acquired at various field strengths to identify possible spectral differences and discriminate between pre- and postintervention samples. The loadings from PLS-DA contained the same spectral regions, implying that the same metabolites were involved in the discrimination independent of magnetic field strength. The investigation revealed a strong increase in prediction performance and thereby spectral information content when increasing the magnetic field strength from 250 to 500 MHz, while from 500 to 800 MHz the increase was less pronounced.

1H NMR study of the effects of sample contamination in the metabolomic analysis of mouse urine

Nuclear magnetic resonance (NMR) spectroscopy was used to evaluate and optimize the strategy for collecting mouse urine samples. A series of normal urine samples and those mixed with folate-deficient food, turkey or mouse fecal particles were analyzed using principal component analysis (PCA). The metabolic profile of urine mixed with folate-deficient food was found to be extremely different than that of clean urine. Changes in the urine composition caused by mixing with turkey or feces are relatively small as judged by the output of PCA. As a result, turkey may be considered as an applicable food source for obtaining uncontaminated urine samples for metabolomics-based research.

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

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

Chemometrics in metabonomics

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