1H NMR spectra of normal urines: reference ranges of the major metabolites

Slice through the water-Exploring the fundamental challenge of water suppression for benchtop NMR systems

Benchtop NMR provides improved accessibility in terms of cost, space, and technical expertise. In turn, this encourages new users into the field of NMR spectroscopy. Unfortunately, many interesting samples in education and research, from beer to whole blood, contain significant amounts of water that require suppression in 1H NMR in order to recover sample information. However, due to the significant reduction in chemical shift dispersion in benchtop NMR systems, the sample signals are much closer to the water resonance compared to those in a corresponding high-field NMR spectrum. Therefore, simply translating solvent suppression experiments intended for high-field NMR instruments to benchtop NMR systems without careful consideration can be problematic. In this study, the effectiveness of several popular water suppression schemes was evaluated for benchtop NMR applications. Emphasis is placed on pulse sequences with no, or few, adjustable parameters making them easy to implement. These fall into two main categories: (1) those based on Pre-SAT including Pre-SAT, PURGE, NOESY-PR, and g-NOESY-PR and (2) those based on binomial inversion including JRS and W5-WATERGATE. Among these schemes, solvent suppression sequences based on Pre-SAT offer a general approach for easy solvent suppression for samples with higher analyte concentrations (sucrose standard and Redbull™). However, for human urine, binomial-like sequences were required. In summary, it is demonstrated that highly efficient water suppression approaches can be implemented on benchtop NMR systems in a simple manner, despite the limited spectral dispersion, further illustrating the potential for widespread implementation of these approaches in education and research.

Emerging urinary alpha-synuclein and miRNA biomarkers in Parkinson's disease

Parkinson's disease (PD) is one of the most common neurodegenerative diseases after Alzheimer's disease (AD), afflicting adults above the age of sixty irrespective of gender, race, ethnicity, and social status. PD is characterized by motor dysfunctions, displaying resting tremor, rigidity, bradykinesia, and postural imbalance. Non-motor symptoms, including rapid eye movement (REM) behavior disorder, constipation, and loss of sense of smell, typically occur many years before the appearance of the PD motor symptoms that lead to a diagnosis. The loss of dopaminergic neurons in the substantia nigra, which leads to the motor symptoms seen in PD, is associated with the deposition of aggregated, misfolded α-Synuclein (α-Syn, SNCA) proteins forming Lewy Bodies. Additionally, dysregulation of miRNA (a short form of mRNA) may contribute to the developing pathophysiology in PD and other diseases such as cancer. Overexpression of α-Syn and miRNA in human samples has been found in PD, AD, and dementia. Therefore, evaluating these molecules in urine, present either in the free form or in association with extracellular vesicles of biological fluids, may lead to early biomarkers for clinical diagnosis. Collection of urine is non-invasive and thus beneficial, particularly in geriatric populations, for biomarker analysis. Considering the expression and function of α-Syn and miRNA, we predict that they can be used as early biomarkers in the diagnosis and prognosis of neurodegenerative diseases.

Balancing the Equation: A Natural History of Trimethylamine and Trimethylamine-N-oxide

Trimethylamine (TMA) and its N-oxide (TMAO) are ubiquitous in prokaryote and eukaryote organisms as well as in the environment, reflecting their fundamental importance in evolutionary biology, and their diverse biochemical functions. Both metabolites have multiple biological roles including cell-signaling. Much attention has focused on the significance of serum and urinary TMAO in cardiovascular disease risk, yet this is only one of the many facets of a deeper TMA-TMAO partnership that reflects the significance of these metabolites in multiple biological processes spanning animals, plants, bacteria, and fungi. We report on analytical methods for measuring TMA and TMAO and attempt to critically synthesize and map the global functions of TMA and TMAO in a systems biology framework.

1H-Nuclear Magnetic Resonance Analysis of Urine as Diagnostic Tool for Organic Acidemias and Aminoacidopathies

The utility of low-resolution 1H-NMR analysis for the identification of biomarkers provided evidence for rapid biochemical diagnoses of organic acidemia and aminoacidopathy. 1H-NMR, with a sensitivity expected for a field strength of 400 MHz at 64 scans was used to establish the metabolomic urine sample profiles of an infant population diagnosed with small molecule Inborn Errors of Metabolism (smIEM) compared to unaffected individuals. A qualitative differentiation of the 1H-NMR spectral profiles of urine samples obtained from individuals affected by different organic acidemias and aminoacidopathies was achieved in combination with GC-MS. The smIEM disorders investigated in this study included phenylalanine metabolism; isovaleric, propionic, 3-methylglutaconicm and glutaric type I acidemia; and deficiencies in medium chain acyl-coenzyme and holocarboxylase synthase. The observed metabolites were comparable and similar to those reported in the literature, as well as to those detected with higher-resolution NMR. In this study, diagnostic marker metabolites were identified for the smIEM disorders. In some cases, changes in metabolite profiles differentiated post-treatments and follow-ups while allowing for the establishment of different clinical states of a biochemical disorder. In addition, for the first time, a 1H-NMR-based biomarker profile was established for holocarboxylase synthase deficiency spectrum.

Adaptive Modelling of Mutated FMO3 Enzyme Could Unveil Unexplored Scenarios Linking Variant Haplotypes to TMAU Phenotypes

Background: Trimethylaminuria (TMAU) is a rare genetic disease characterized by the accumulation of trimethylamine (TMA) and its subsequent excretion trough main body fluids, determining the characteristic fish odour in affected patients. We realized an experimental study to investigate the role of several coding variants in the causative gene FMO3, that were only considered as polymorphic or benign, even if the available literature on them did not functionally explain their ineffectiveness on the encoded enzyme. Methods: Mutational analysis of 26 TMAU patients was realized by Sanger sequencing. Detected variants were, subsequently, deeply statistically and in silico characterized to determine their possible effects on the enzyme activity. To achieve this goal, a docking prediction for TMA/FMO3 and an unbinding pathway study were performed. Finally, a TMAO/TMA urine quantification by 1H-NMR spectroscopy was performed to support modelling results. Results: The FMO3 screening of all patients highlighted the presence of 17 variants distributed in 26 different haplotypes. Both non-sense and missense considered variants might impair the enzymatic kinetics of FMO3, probably reducing the interaction time between the protein catalytic site and TMA, or losing the wild-type binding site. Conclusions: Even if further functional assays will confirm our predictive results, considering the possible role of FMO3 variants with still uncertain effects, might be a relevant step towards the detection of novel scenarios in TMAU etiopathogenesis.

Monitoring Methylmalonic Aciduria by NMR Urinomics

The paper reports on monitoring methylmalonic aciduria (MMA)-specific and non-specific metabolites via NMR urinomics. Five patients have been monitored over periods of time; things involved were diet, medication and occasional episodes of failing to comply with prescribed diets. An extended dataset of targeted metabolites is presented, and correlations with the type of MMA are underlined. A survey of previous NMR studies on MMA is also presented.

Diagnosis and phenotypic assessment of trimethylaminuria, and its treatment with riboflavin: 1H NMR spectroscopy and genetic testing

Background

Trimethylaminuria (TMAU) is a metabolic disorder characterized by the excessive excretion of the malodorous compound trimethylamine (TMA). The diagnosis of TMAU is challenging because this disorder is situated at the boundary between biochemistry and psychiatry. Here, we used nuclear magnetic resonance spectroscopy to assess TMAU in 13 patients. We also sequenced the FMO3 gene in 11 of these patients. Treatment with vitamin B2 was prescribed.

Results

Two patients (aged 3 and 9 years at the initial consultation) had a particularly unpleasant body odor, as assessed by their parents and the attending physicians. The presence of high urine TMA levels confirmed the presence of a metabolic disorder. The two (unrelated) children carried compound heterozygous variants in the FMO3 gene. In both cases, vitamin B2 administration decreased TMA excretion and reduced body odor. The 11 adults complained of an unpleasant body odor, but the physicians did not confirm this. In all adult patients, the urine TMA level was within the normal range reported for control (non-affected) subjects, although two of the patients displayed an abnormally high proportion of oxidized TMA. Seven of the 9 tested adult patients had a hypomorphic variant of the FMO3 gene; the variant was found in the homozygous state, in the heterozygous state or combined with another hypomorphic variant. All 11 adults presented a particular psychological or psychiatric phenotype, with a subjective perception of unpleasant odor.

Conclusions

The results present the clinical and biochemical data of patients complaining of unpleasant body odor. Contrary to adult patients, the two children exhibited all criteria of recessively inherited trimethylaminuria, suspected by parents in infancy. B2 vitamin treatment dramatically improved the unpleasant body odor and the ratio of TMA/Cr vs TMAO/Cr in the urine in the children. Other patients presented a particular psychological or psychiatric phenotype.

Fast LC-ESI-MS/MS analysis and influence of sampling conditions for gut metabolites in plasma and serum

In the past few years, the gut microbiome has been shown to play an important role in various disorders including in particular cardiovascular diseases. Especially the metabolite trimethylamine-N-oxide (TMAO), which is produced by gut microbial metabolism, has repeatedly been associated with an increased risk for cardiovascular events. Here we report a fast liquid chromatography tandem mass spectrometry (LC-MS/MS) method that can analyze the five most important gut metabolites with regards to TMAO in three minutes. Fast liquid chromatography is unconventionally used in this method as an on-line cleanup step to remove the most important ion suppressors leaving the gut metabolites in a cleaned flow through fraction, also known as negative chromatography. We compared different blood matrix types to recommend best sampling practices and found citrated plasma samples demonstrated lower concentrations for all analytes and choline concentrations were significantly higher in serum samples. We demonstrated the applicability of our method by investigating the effect of a standardized liquid meal (SLM) after overnight fasting of 25 healthy individuals on the gut metabolite levels. The SLM did not significantly change the levels of gut metabolites in serum.

Metabolomic and biochemical characterization of a new model of the transition of acute kidney injury to chronic kidney disease induced by folic acid

Background

Renal diseases represent a major public health problem. The demonstration that maladaptive repair of acute kidney injury (AKI) can lead to the development of chronic kidney disease (CKD) and end-stage renal disease has generated interest in studying the pathophysiological pathways involved. Animal models of AKI–CKD transition represent important tools to study this pathology. We hypothesized that the administration of multiple doses of folic acid (FA) would lead to a progressive loss of renal function that could be characterized through biochemical parameters, histological classification and nuclear magnetic resonance (NMR) profiling.

Methods

Wistar rats were divided into groups: the control group received a daily intraperitoneal (I.P.) injection of double-distilled water, the experimental group received a daily I.P. injection of FA (250 mg kg body weight⁻¹). Disease was classified according to blood urea nitrogen level: mild (40–80 mg dL⁻¹), moderate (100–200 mg dL⁻¹) and severe (>200 mg dL⁻¹). We analyzed through biochemical parameters, histological classification and NMR profiling.

Results

Biochemical markers, pro-inflammatory cytokines and kidney injury biomarkers differed significantly (P < 0.05) between control and experimental groups. Histology revealed that as damage progressed, the degree of tubular injury increased, and the inflammatory infiltrate was more evident. NMR metabolomics and chemometrics revealed differences in urinary metabolites associated with CKD progression. The main physiological pathways affected were those involved in energy production and amino-acid metabolism, together with organic osmolytes. These data suggest that multiple administrations of FA induce a reproducible model of the induction of CKD. This model could help to evaluate new strategies for nephroprotection that could be applied in the clinic.

Amperometric Detection of the Urinary Disease Biomarker p-HPA by Allosteric Modulation of a Redox Polymer-Embedded Bacterial Reductase

We report an amperometric biosensor for the urinary disease biomarker para-hydroxyphenylacetate ( p-HPA) in which the allosteric reductase component of a bacterial hydroxylase, C1-hpah, is electrically wired to glassy carbon electrodes through incorporation into a low-potential Os-complex modified redox polymer. The proposed biosensing strategy depends on allosteric modulation of C1-hpah by the binding of the enzyme activator and analyte p-HPA, stimulating oxidation of the cofactor NADH. The pronounced concentration-dependence of allosteric C1-hpah modulation in the presence of a constant concentration of NADH allowed sensitive quantification of the target, p-HPA. The specific design of the immobilizing redox polymer with suitably low working potential allowed biosensor operation without the risk of co-oxidation of potentially interfering substances, such as uric acid or ascorbic acid. Optimized sensors were successfully applied for p-HPA determination in artificial urine, with good recovery rates and reproducibility and sub-micromolar detection limits. The proposed application of the allosteric enzyme C1-hpah for p-HPA trace electroanalysis is the first successful example of simple amperometric redox enzyme/redox polymer biosensing in which the analyte acts as an effector, modulating the activity of an immobilized biocatalyst. A general advantage of the concept of allosterically modulated biosensing is its ability to broaden the range of approachable analytes, through the move from substrate to effector detection.

An update of urine and blood metabolomics in chronic kidney disease

Chronic kidney disease is considered as a serious obstacle in global health, with increasing incidence and prevalence. In spite of numerous attempts by using recent omics technologies, specially metabolomics, for understanding pathophysiology, molecular mechanism and identification reliable consensus biomarkers for diagnosis and prognosis of this complex disease, the current biomarkers are still insensitive and many questions about its pathomechanism are still to be unanswered. This review is focused on recent findings about urine and serum/plasma metabolite biomarkers and changes in the pathways that occurs in the disease conditions. The urine and blood metabolome content in the normal and disease state is investigated based on the current metabolomics studies and well known metabolite candidate biomarkers for chronic kidney disease are discussed.

Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects

PURPOSE

Differences in resting energy expenditure (REE) between men and women mainly result from sex-related differences in lean body mass (LBM). So far, a little is known about whether REE and LBM are reflected by a distinct human metabolite profile. Therefore, we aimed to identify plasma and urine metabolite patterns that are associated with REE and LBM of healthy subjects.

METHODS

We investigated 301 healthy male and female subjects (18-80 years) under standardized conditions in the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study. REE was determined by indirect calorimetry and LBM by dual X-ray absorptiometry. Fasting blood and 24 h urine samples were analyzed by targeted and non-targeted metabolomics methods using GC × GC-MS, GC-MS, LC-MS, and NMR. Data were evaluated by predictive modeling of combined data using different machine learning algorithms, namely SVM, glmnet, and PLS.

RESULTS

When evaluating data of men and women combined, we were able to predict REE and LBM with high accuracy (> 90%). This, however, was a clear effect of sex, which is supported by the high degree of overlap in identified important metabolites for LBM, REE, and sex, respectively. The applied machine learning algorithms did not reveal a metabolite pattern predictive of REE or LBM, when analyzing data for men and women, separately.

CONCLUSIONS

We could not identify a sex independent predictive metabolite pattern for REE or LBM. REE and LBM have no impact on plasma and urine metabolite profiles in the KarMeN Study participants. Studies applying metabolomics in healthy humans need to consider sex specific data evaluation.

Serum and urine toxicometabolomics following gentamicin-induced nephrotoxicity in male Sprague-Dawley rats

Gentamicin (GM) is an aminoglycoside antibiotic used in treatment of various types of bacterial infections, but the major adverse effect is drug-induced nephrotoxicity. This study aimed to determine biomarkers that might predict nephrotoxicity initiated by GM using serum or urinary proton nuclear magnetic resonance (¹H NMR) spectral data in male Sprague-Dawley rats. GM (0, 30, or 300 mg/kg/d) was intraperitoneally administered for 3 consecutive days. Animals were sacrificed 2 d (D2) or 8 d (D8) after last administration of GM in order to perform analysis of serum biochemistries and histopathologic examination. Urine samples were collected every 24 h from prior to treatment until sacrifice. Serum and urinary ¹H NMR spectral data revealed apparent differential clustering between control and GM-treated groups as evidenced by principal component analysis (PCA) and orthogonal projections to latent structure-discriminant analysis (OPLS-DA) in global and targeted profiling. The concentrations of endogenous serum metabolites including 3-hydroxybutyrate, alanine, citrate, creatine, glucose, and glycine were increased significantly on D2 or D8. Urinary levels of glucose, glycine, and succinate were significantly elevated on D2 or D8, whereas the concentration of hippurate was significantly decreased on D2 and D8. Correlation of serum and urinary ¹H NMR OPLS-DA with serum biochemistry and renal histopathologic changes suggests that ¹H NMR urinalysis may be used to reliably predict or screen for GM-induced nephrotoxicity. In contrast, Western blot analysis of kidney injury molecule-1 (KIM-1) demonstrated that protein expression was not markedly altered indicating this biomarker was not sensitive to detect GM-mediated renal damage. Data suggest that these altered metabolites might serve as specific and sensitive biomarkers for GM-mediated renal damage.

The utility of biomarkers in hepatocellular carcinoma: review of urine-based 1H-NMR studies - what the clinician needs to know

Hepatocellular carcinoma (HCC) is the fifth most common malignancy, the third most common cause of cancer death, and the most common primary liver cancer. Overall, there is a need for more reliable biomarkers for HCC, as those currently available lack sensitivity and specificity. For example, the current gold-standard biomarker, serum alpha-fetoprotein, has a sensitivity of roughly only 70%. Cancer cells have different characteristic metabolic signatures in biofluids, compared to healthy cells; therefore, metabolite analysis in blood or urine should lead to the detection of suitable candidates for the detection of HCC. With the advent of metabonomics, this has increased the potential for new biomarker discovery. In this article, we look at approaches used to identify biomarkers of HCC using proton nuclear magnetic resonance (¹H-NMR) spectroscopy of urine samples. The various multivariate statistical analysis techniques used are explained, and the process of biomarker identification is discussed, with a view to simplifying the knowledge base for the average clinician.

Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study

Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles.

In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18–80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms.

Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy.

The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.

Assessment of metabolic phenotypic variability in children's urine using 1H NMR spectroscopy

The application of metabolic phenotyping in clinical and epidemiological studies is limited by a poor understanding of inter-individual, intra-individual and temporal variability in metabolic phenotypes. Using 1H NMR spectroscopy we characterised short-term variability in urinary metabolites measured from 20 children aged 8-9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%). Pooled samples captured the best inter-individual variability with an ICC of 0.40 (median). Trimethylamine, N-acetyl neuraminic acid, 3-hydroxyisobutyrate, 3-hydroxybutyrate/3-aminoisobutyrate, tyrosine, valine and 3-hydroxyisovalerate exhibited the highest stability with over 50% of variance specific to the child. The pooled sample was shown to capture the most inter-individual variance in the metabolic phenotype, which is of importance for molecular epidemiology study design. A substantial proportion of the variation in the urinary metabolome of children is specific to the individual, underlining the potential of such data to inform clinical and exposome studies conducted early in life.

Assessment of 1H NMR-based metabolomics analysis for normalization of urinary metals against creatinine

BACKGROUND

Proton nuclear magnetic resonance (¹H NMR, or NMR) spectroscopy and inductively coupled plasma-mass spectrometry (ICP-MS) are commonly used for metabolomics and metal analysis in urine samples. However, creatinine quantification by NMR for the purpose of normalization of urinary metals has not been validated. We assessed the validity of using NMR analysis for creatinine quantification in human urine samples in order to allow normalization of urinary metal concentrations.

METHODS

NMR and ICP-MS techniques were used to measure metabolite and metal concentrations in urine samples from 10 healthy subjects. For metabolite analysis, two magnetic field strengths (600 and 700MHz) were utilized. In addition, creatinine concentrations were determined by using the Jaffe method.

RESULTS

Creatinine levels were strongly correlated (R²=0.99) between NMR and Jaffe methods. The NMR spectra were deconvoluted with a target database containing 151 metabolites that are present in urine. A total of 50 metabolites showed good correlation (R²=0.7-1.0) at 600 and 700MHz. Metal concentrations determined after NMR-measured creatinine normalization were comparable to previous reports.

CONCLUSIONS

NMR analysis provided robust urinary creatinine quantification, and was sufficient for normalization of urinary metal concentrations. We found that NMR-measured creatinine-normalized urinary metal concentrations in our control subjects were similar to general population levels in Canada and the United Kingdom.

A GC-MS urinary quantitative metabolomics analysis in depressed patients treated with TCM formula of Xiaoyaosan

Xiaoyaosan, one of the best-known traditional Chinese medicine prescriptions, has been widely used in China for the treatment of mental disorders such as depression. Although both clinical application and animal experiments indicate that Xiaoyaosan has an obvious antidepressant effect, the mechanism still remains unclarified, and there are few studies quantitatively measured the biomarkers of Xiaoyaosan treatment by metabolomics to determination. In this study, 25 depressed patients and 33 healthy volunteers were recruited. A GC-MS based metabolomics approach and the multivariate statistical methods were used for analyzing the urine metabolites of depressed patients before and after treatment compared with healthy controls. Then the biomakers through metabolomics determination were carried out the quantitative analysis. In total, 5 metabolites were identified as the potential diseased and therapeutic biomarkers of depression and Xiaoyaosan. Alanine, citrate and hippurate levels were significantly increased in the urine samples from depressed patients compared with healthy controls, while phenylalanie and tyrosine levels were significantly decreased. However, after Xiaoyaosan treatment for 6 weeks, phenylalanie and tyrosine levels were significantly increased (p<0.05) and alanine, citrate and hippurate levels significantly decreased (p<0.05). Xiaoyaosan has a good priority on the treatment of depression and the ability to adjust the neurotransmitters to obtain the best treated response and also could regulate the metabolism of amino acids and promote to produce energy meet the needs of the body.

Sample normalization methods in quantitative metabolomics

To reveal metabolomic changes caused by a biological event in quantitative metabolomics, it is critical to use an analytical tool that can perform accurate and precise quantification to examine the true concentration differences of individual metabolites found in different samples. A number of steps are involved in metabolomic analysis including pre-analytical work (e.g., sample collection and storage), analytical work (e.g., sample analysis) and data analysis (e.g., feature extraction and quantification). Each one of them can influence the quantitative results significantly and thus should be performed with great care. Among them, the total sample amount or concentration of metabolites can be significantly different from one sample to another. Thus, it is critical to reduce or eliminate the effect of total sample amount variation on quantification of individual metabolites. In this review, we describe the importance of sample normalization in the analytical workflow with a focus on mass spectrometry (MS)-based platforms, discuss a number of methods recently reported in the literature and comment on their applicability in real world metabolomics applications. Sample normalization has been sometimes ignored in metabolomics, partially due to the lack of a convenient means of performing sample normalization. We show that several methods are now available and sample normalization should be performed in quantitative metabolomics where the analyzed samples have significant variations in total sample amounts.

Does our gut microbiome predict cardiovascular risk? A review of the evidence from metabolomics

Millions of microbes are found in the human gut, and are collectively referred as the gut microbiota. Recent studies have estimated that the microbiota genome contains 100-fold more genes than the host genome. These microbiota contribute to digestion by processing energy substrates unutilized by the host, with about half of the total genome of the gut microbiota being related to central carbon and amino acid metabolism as well as the biosynthesis of secondary metabolites. Therefore, the gut microbiome and its interaction with the host influences many aspects of health and disease, including the composition of biofluids such as urine and blood plasma. Metabolomics is uniquely suited to capture these functional host-microbe interactions. This review aims at providing an overview of recent metabolomics evidence of gut microbiota-host metabolic interactions with a specific focus on cardiovascular disease and related aspects of the metabolic syndrome. Furthermore, the emphasis is given on the complexities of translating these metabolite signatures as potential clinical biomarkers, as the composition and activity of gut microbiome change with many factors, particularly with diet, with special reference to trimethylamine-oxide.

Non-targeted analyses of organic compounds in urban wastewater

A large number of organic pollutants that cause damage to the ecosystem and threaten human health are transported to wastewater treatment plants (WWTPs). The problems regarding water pollution in Latin America have been well documented, and there is no evidence of substantive efforts to change the situation. In the present work, two methods to study wastewater samples are employed: non-targeted 1D ((13)C and (1)H) and 2D NMR spectroscopic analysis to characterize the largest possible number of compounds from urban wastewater and analysis by HPLC-(UV/MS)-SPE-ASS-NMR to detect non-specific recalcitrant organic compounds in treated wastewater without the use of common standards. The set of data is composed of several compounds with the concentration ranging considerably with treatment and seasonality. An anomalous discharge, the influence of stormwater on the wastewater composition and the presence of recalcitrant compounds (linear alkylbenzene sulfonate surfactant homologs) in the effluent were further identified. The seasonal variations and abnormality in the composition of organic compounds in sewage indicated that the procedure that was employed can be useful in the identification of the pollution source and to enhance the effectiveness of WWTPs in designing preventive action to protect the equipment and preserve the environment.

Strategy for NMR metabolomic analysis of urine in mouse models of obesity--from sample collection to interpretation of acquired data

The mouse model of monosodium glutamate induced obesity was used to examine and consequently optimize the strategy for analysis of urine samples by NMR spectroscopy. A set of nineteen easily detectable metabolites typical in obesity-related studies was selected. The impact of urine collection protocol, choice of (1)H NMR pulse sequence, and finally the impact of the normalization method on the detected concentration of selected metabolites were investigated. We demonstrated the crucial effect of food intake and diurnal rhythms resulting in the choice of a 24-hour fasting collection protocol as the most convenient for tracking obesity-induced increased sensitivity to fasting. It was shown that the Carr-Purcell-Meiboom-Gill (CPMG) experiment is a better alternative to one-dimensional nuclear Overhauser enhancement spectroscopy (1D-NOESY) for NMR analysis of mouse urine due to its ability to filter undesirable signals of proteins naturally present in rodent urine. Normalization to total spectral area provided comparable outcomes as did normalization to creatinine or probabilistic quotient normalization in the CPMG-based model. The optimized approach was found to be beneficial mainly for low abundant metabolites rarely monitored due to their overlap by strong protein signals.

Chronic toxicity of crude ricinine in rats assessed by1H NMR metabolomics analysis

A¹H-NMR based metabolomics approach combined with OSC-PLS-DA was applied to investigate the chronic toxicity of crude ricinine from castor bean shell in rats for the first time.

Metabolomic fingerprinting: challenges and opportunities

Systems biology has primarily focused on studying genomics, transcriptomics, and proteomics and their dynamic interactions. These, however, represent only the potential for a biological outcome since the ultimate phenotype at the level of the eventually produced metabolites is not taken into consideration. The emerging field of metabolomics provides complementary guidance toward an integrated approach to this problem: It allows global profiling of the metabolites of a cell, tissue, or host and presents information on the actual end points of a response. A wide range of data collection methods are currently used and allow the extraction of global or tissue-specific metabolic profiles. The great amount and complexity of data that are collected require multivariate analysis techniques, but the increasing amount of work in this field has made easy-to-use analysis programs readily available. Metabolomics has already shown great potential in drug toxicity studies, disease modeling, and diagnostics and may be integrated with genomic and proteomic data in the future to provide in-depth understanding of systems, pathways, and their functionally dynamic interactions. In this review we discuss the current state of the art of metabolomics, its applications, and future potential.

Prospective evaluation of potential toxicity of repeated doses of Thymus vulgaris L. extracts in rats by means of clinical chemistry, histopathology and NMR-based metabonomic approach

In the field of natural extracts, research generally focuses on the study of their biological activities for food, cosmetic, or pharmacological purposes. The evaluation of their adverse effects is often overlooked. In this study, the extracts of Thymus vulgaris L. were obtained by two different extraction methods. Intraperitoneal injections of both extracts were given daily for four days to male Wistar Han rats, at two different doses for each extract. The evaluation of the potential toxic effects included histopathological examination of liver, kidney, and lung tissues, as well as serum biochemistry of liver and kidney parameters, and (1)H-NMR-based metabonomic profiles of urine. The results showed that no histopathological changes were observed in the liver and kidney in rats treated with both extracts of thyme. Serum biochemical investigations revealed significant increases in blood urea nitrogen, creatinine, and uric acid in animals treated with polyphenolic extract at both doses. In these latter groups, metabonomic analysis revealed alterations in a number of urine metabolites involved in the energy metabolism in liver mitochondria. Indeed, the results showed alterations of glycolysis, Krebs cycle, and β-oxidative pathways as evidenced by increases in lactate and ketone bodies, and decreases in citrate, α-ketoglutarate, creatinine, hippurate, dimethylglycine, and dimethyalanine. In conclusion, this work showed that i.p. injection of repeated doses of thyme extracts causes some disturbances of intermediary metabolism in rats. The metabonomic study revealed interesting data which could be further used to determine the cellular pathways affected by such treatments.

Nutrimetabonomics: nutritional applications of metabolic profiling

An individual's metabolic phenotype, and ultimately health, is significantly influenced by complex interactions between their genes and the diet. Studying these associations and their downstream biochemical consequences has proven extremely challenging using traditional hypothesis-led strategies. Metabonomics, a systems biology approach, allows the global metabolic response of biological systems to stimuli to be characterised. Through the application of this approach to nutritional-based research, nutrimetabonomics, the biochemical response to dietary inputs is being investigated at greater levels of resolution. This has allowed novel insights to be gained regarding intricate diet-gene interactions and their consequences for health and disease. In this review, we present some of the latest research exploring how nutrimetabonomics can assist in the elucidation of novel biomarkers of dietary behaviour and provide new perspectives on diet-health relationships. The use of this approach to study the metabolic interplay between the gut microbiota and the host is also explored.

Metabolomic profiling of tumor-bearing mice

Metabolomics is one of the newcomers among the "omics" techniques, perhaps also constituting the most relevant for the study of pathophysiological conditions. Metabolomics may indeed yield not only disease-specific biomarkers but also profound insights into the etiology and progression of a variety of human disorders. Various metabolomic approaches are currently available to study oncogenesis and tumor progression in vivo, in murine tumor models. Many of these models rely on the xenograft of human cancer cells into immunocompromised mice. Understanding how the metabolism of these cells evolves in vivo is critical to evaluate the actual pertinence of xenograft models to human pathology. Here, we discuss various tumor xenograft models and methods for their metabolomic profiling to provide a short guide to investigators interested in this field of research.

Nuclear spin singlet states as a contrast mechanism for NMR spectroscopy

Nuclear magnetic resonance (NMR) spectra of complex chemical mixtures often contain unresolved or hidden spectral components, especially when strong background signals overlap weaker peaks. In this article we demonstrate a quantum filter utilizing nuclear spin singlet states, which allows undesired NMR spectral background to be removed and target spectral peaks to be uncovered. The quantum filter is implemented by creating a nuclear spin singlet state with spin quantum numbers j = 0, mz  = 0 in a target molecule, applying a continuous RF field to both preserve the singlet state and saturate the magnetization of undesired molecules and then mapping the target molecule singlet state back into an NMR observable state so that its spectrum can be read out unambiguously. The preparation of the target singlet state can be carefully controlled with pulse sequence parameters, so that spectral contrast can be achieved between molecules with very similar structures. We name this NMR contrast mechanism 'Suppression of Undesired Chemicals using Contrast-Enhancing Singlet States' (SUCCESS) and we demonstrate it in vitro for three target molecules relevant to neuroscience: aspartate, threonine and glutamine.

The human urine metabolome

Urine has long been a "favored" biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca.

(1)H-NMR-based metabolomic analysis of the effect of moderate wine consumption on subjects with cardiovascular risk factors

Moderate wine consumption is associated with health-promoting activities. An H-NMR-based metabolomic approach was used to identify urinary metabolomic differences of moderate wine intake in the setting of a prospective, randomized, crossover, and controlled trial. Sixty-one male volunteers with high cardiovascular risk factors followed three dietary interventions (28 days): dealcoholized red wine (RWD) (272mL/day, polyphenol control), alcoholized red wine (RWA) (272mL/day) and gin (GIN) (100mL/day, alcohol control). After each period, 24-h urine samples were collected and analyzed by (1) H-NMR. According to the results of a one-way ANOVA, significant markers were grouped in four categories: alcohol-related markers (ethanol); gin-related markers; wine-related markers; and gut microbiota markers (hippurate and 4-hydroxphenylacetic acid). Wine metabolites were classified into two groups; first, metabolites of food metabolome: tartrate (RWA and RWD), ethanol, and mannitol (RWA); and second, biomarkers that relates to endogenous modifications after wine consumption, comprising branched-chain amino acid (BCAA) metabolite (3-methyl-oxovalerate). Additionally, a possible interaction between alcohol and gut-related biomarkers has been identified. To our knowledge, this is the first time that this approach has been applied in a nutritional intervention with red wine. The results show the capacity of this approach to obtain a comprehensive metabolome picture including food metabolome and endogenous biomarkers of moderate wine intake.

Advances in gas chromatographic methods for the identification of biomarkers in cancer

Screening complex biological specimens such as exhaled air, tissue, blood and urine to identify biomarkers in different forms of cancer has become increasingly popular over the last decade, mainly due to new instruments and improved bioinformatics. However, despite some progress, the identification of biomarkers has shown to be a difficult task with few new biomarkers (excluding recent genetic markers) being considered for introduction to clinical analysis. This review describes recent advances in gas chromatographic methods for the identification of biomarkers in the detection, diagnosis and treatment of cancer. It presents a general overview of cancer metabolism, the current biomarkers used for cancer diagnosis and treatment, a background to metabolic changes in tumors, an overview of current GC methods, and collectively presents the scope and outlook of GC methods in oncology.

Metabolomics of urinary tract infection: a new uroscope in town

Urinary tract infection (UTI) is a potentially life-threatening infectious disease. For rapid directed therapy of UTIs, it is essential to determine the causative microorganism. To date, there is no single test that has been proven to reliably, rapidly and accurately identify the etiologic organism in UTI. The molecular methods for diagnosing the cause of UTI and prognostic development of clinically important metabolomic evaluations and their limitations for use in the diagnosis and monitoring of infections are discussed in this review article. The application of the emerging investigative device NMR spectroscopy as a surrogate method for the diagnosis of UTI is also addressed.

A Preliminary Investigation of NSCL/P Plasma and Urine in Guizhou Province in China Using NMR-Based Metabonomics

Objective : To assess the feasibility of metabonomics in clinical studies. This is a pilot study introducing nuclear magnetic resonance (NMR)-based metabonomics to elucidate and compare the metabolism of patients with nonsyndromic cleft lip and/or palate (NSCL/P) and children without orofacial clefts. Methods : High-resolution (1)H NMR spectroscopy was performed on plasma and urine samples obtained from NSCL/P and healthy children. The (1)H NMR spectra were further analyzed with principal component analysis. Results : Compared to the control group, the level of low-molecular-weight metabolites in plasma such as asparagine was higher in NSCL/P patients, while arginine, lysine, acetate, lactate, proline, glutamine, pyruvate, creatinine, choline, and β-glucose were lower. The carnitine, citrate, and formate excretion in urine appeared to be higher in the healthy children, while the NSCL/P group excreted higher concentrations of aspartic acid and phenylalanine in urine. Conclusion : The present study clearly demonstrated the great potential of NMR-based metabonomics in elucidating NSCL/P plasma metabolism and the possible application of this technology in clinical diagnosis and screening.

Biofluid metabonomics using (1)H NMR spectroscopy: the road to biomarker discovery in gastroenterology and hepatology

Metabolic profiling or 'metabonomics' is an investigatory method that allows metabolic changes associated with the presence of an underlying pathological process to be investigated. Various biofluids can be utilized in the process but urine, serum and fecal extract are most pertinent to the investigation of gastrointestinal and hepatological disease. Nuclear magnetic resonance spectroscopy-based metabonomic research has the potential to generate novel noninvasive diagnostic tests, based on biomarkers of disease, which are simple and cost effective yet retain high sensitivity and specificity characteristics. The process involves a number of key steps, including sample collection, data acquisition, chemometric techniques and, finally, validation. This technique-driven review aims to demystify the metabonomics pathway, while also illustrating the potential of this technique with recent examples of its application in hepato-gastroenterological disease.

Nuclear magnetic resonance-based metabolomics for prediction of gastric damage induced by indomethacin in rats

Non-steroidal anti-inflammatory drugs (NSAIDs) have side effects including gastric erosions, ulceration and bleeding. In this study, pattern recognition analysis of the (1)H-nuclear magnetic resonance (NMR) spectra of urine was performed to develop surrogate biomarkers related to the gastrointestinal (GI) damage induced by indomethacin in rats. Urine was collected for 5 h after oral administration of indomethacin (25 mg kg(-1)) or co-administration with cimetidine (100 mg kg(-1)), which protects against GI damage. The (1)H-NMR urine spectra were divided into spectral bins (0.04 ppm) for global profiling, and 36 endogenous metabolites were assigned for targeted profiling. The level of gastric damage in each animal was also determined. Indomethacin caused severe gastric damage; however, indomethacin administered with cimetidine did not. Simultaneously, the patterns of changes in their endogenous metabolites were different. Multivariate data analyses were carried out to recognize the spectral pattern of endogenous metabolites related to indomethacin using partial least square-discrimination analysis. In targeted profiling, a few endogenous metabolites, 2-oxoglutarate, acetate, taurine and hippurate, were selected as putative biomarkers for the gastric damage induced by indomethacin. These metabolites changed depending on the degree of GI damage, although the same dose of indomethacin (10 mg kg(-1)) was administered to rats. The results of global and targeted profiling suggest that the gastric damage induced by NSAIDs can be screened in the preclinical stage of drug development using a NMR based metabolomics approach.

A metabonomic study of biochemical changes characteristic of genetically hypertensive rats based on (1)H NMR spectroscopic urinalysis

Spontaneously hypertensive rats (SHR) provide a simple model for studying essential hypertension. Their genetic and metabolic features are of great interest because they may provide insights into the pathophysiological processes underlying essential hypertension. We have thus investigated the metabolic characteristics of SHR at various ages, covering the prehypertensive stage and the developmental phase of hypertension, using a (1)H nuclear magnetic resonance (NMR)-based metabonomic approach. Twenty-four-hour urine samples from the SHR and their age-matched normotensive control, Wistar-Kyoto rats, were analyzed using (1)H NMR spectroscopy, and the spectral data were subjected to principal components analysis (PCA) to find metabolic differences between the two strains. Consequently, it was possible to separate the urine samples between the two strains at any age ranging from 4 to 20 weeks in the principal component scores plots. The major spectral regions and signals (metabolites) contributing to the separation were picked up based on the loadings. Subsequently, the urinary excreted levels of metabolites highlighted by the PCA were compared based on the signal intensities corrected by urine volume and body weight. These investigations revealed the major metabolic changes characteristic of the SHR, which included differences in citrate, α-ketoglutarate, succinate, hippurate, phenylacetylglycine, p-cresol glucuronide, creatine, taurine, medium-chain dicarboxylates (tentative), unknown (δ 3.11), and the regions at 3.60, 3.64, 3.68 and 3.88 p.p.m. The results supported the occurrence of metabolic acidosis in the SHR in the period of prehypertension as well as rapidly rising blood pressure. In addition, the intestinal microfloral populations in the SHR were suggested to be altered in the developmental phase of hypertension.

The effect of 1,000 km nonstop cycling on fat mass and skeletal muscle mass

We evaluated the change in body mass including fat mass and skeletal muscle mass in one ultracyclist whilst cycling 1,000 km in 48 hours at a constant intensity of ∼48% VO(2)max, corresponding to a heart rate frequency of ∼105 ± 5 bpm. A 1 kg fat mass decrease resulted, with the largest decrease occurring between the 12th and the 24th hour. No steady state in metabolism was observed and no regular decrease of subcutaneous adipose tissue resulted. This result is backed up by the nuclear magnetic resonance (NMR) urine analysis. Body water increase with simultaneous dehydration is possibly due to endocrine-induced renal water retention, in order to maintain metabolism processes that are required for energy supply and blood flow during very prolonged exercise. Both applied methods, the anthropometric and the bioelectrical impedance analysis, analyse fluid accumulation--especially in the skinfolds of the lower extremities--apparently incorrectly as an increase in body mass and not as an increase in fluids.

Urine metabolomics for kidney cancer detection and biomarker discovery

Renal cell carcinoma (RCC) is one of the few human cancers whose incidence is increasing. The disease regularly progresses asymptomatically and is frequently metastatic upon presentation, thereby necessitating the development of an early method of detection. A metabolomic approach for biomarker detection using urine as a biofluid is appropriate since the tumor is located in close proximity to the urinary space. By comparing the composition of urine from individuals with RCC to control individuals, differences in metabolite composition of this biofluid can be identified, and these data can be utilized to create a clinically applicable and, possibly, bedside assay. Recent studies have shown that sample handling and processing greatly influences the variability seen in the urinary metabolome of both cancer and control patients. Once a standard method of collection is developed, identifying metabolic derangements associated with RCC will also lead to the investigation of novel targets for therapeutic intervention. The objective of this review is to discuss existing methods for sample collection, processing, data analysis, and recent findings in this emerging field.

[Ischemic lesions in kidneys after extracorporeal shock wave lithotripsy demonstrated by Proton NMR spectroscopy of urine samples]

PURPOSE

The aim of this prospective study was to investigate the effect of extracorporeal shock wave lithotripsy (SWL) on kidneys of patients with pyelic stone disease. The effects of SWL were assessed by high-resolution proton nuclear magnetic resonance (HNMR) spectroscopy of urine samples.

METHODS

Twenty-three patients, aged 31-80years (mean: 55years), with pyelic stone disease were investigated before and after SWL. Multiparameter analysis was performed by HNMR spectroscopy of urine samples collected before and 5h after SWL (second miction post-SWL).

RESULTS

The most relevant resonances determined by HNMR spectroscopy were acetate, lactate, trimethylamine N-oxide and amino acids. Excretion of these markers increased significantly in comparison with pre-SWL urinary samples.

CONCLUSION

These results show that early ischemic damage occurs after SWL. Post-SWL. HNMR spectroscopy is an effective tool for noninvasive follow-up of renal damage.