750 MHz 1H NMR spectroscopy characterisation of the complex metabolic pattern of urine from patients with inborn errors of metabolism: 2-hydroxyglutaric aciduria and maple syrup urine disease

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.

1H Nuclear Magnetic Resonance Spectroscopy-Based Methods for the Quantification of Proteins in Urine

We described several postprocessing methods to measure protein concentrations in human urine from existing ¹H nuclear magnetic resonance (NMR) metabolomic spectra: (1) direct spectral integration, (2) integration of NCD spectra (NCD = 1D NOESY-CPMG), (3) integration of SMolESY-filtered 1D NOESY spectra (SMolESY = Small Molecule Enhancement SpectroscopY), (4) matching protein patterns, and (5) TSP line integral and TSP linewidth. Postprocessing consists of (a) removal of the metabolite signals (demetabolization) and (b) extraction of the protein integral from the demetabolized spectra. For demetabolization, we tested subtraction of the spin-echo 1D spectrum (CPMG) from the regular 1D spectrum and low-pass filtering of 1D NOESY by its derivatives (c-SMolESY). Because of imperfections in the demetabolization, in addition to direct integration, we extracted protein integrals by the piecewise comparison of demetabolized spectra with the reference spectrum of albumin. We analyzed 42 urine samples with protein content known from the bicinchoninic acid (BCA) assay. We found excellent correlation between the BCA assay and the demetabolized NMR integrals. We have provided conversion factors for calculating protein concentrations in mg/mL from spectral integrals in mM. Additionally, we found the trimethylsilyl propionate (TSP, NMR standard) spectral linewidth and the TSP integral to be good indicators of protein concentration. The described methods increase the information content of urine NMR metabolomics spectra by informing clinical studies of protein concentration.

Robust Metabolite Quantification from J-Compensated 2D 1H-13C-HSQC Experiments

The spectral resolution of 2D 1H-13C heteronuclear single quantum coherence (1H-13C-HSQC) nuclear magnetic resonance (NMR) spectra facilitates both metabolite identification and quantification in nuclear magnetic resonance-based metabolomics. However, quantification is complicated by variations in magnetization transfer, which among others originate mainly from scalar coupling differences. Methods that compensate for variation in scalar coupling include the generation of calibration factors for individual signals or the use of additional pulse sequence schemes such as quantitative HSQC (Q-HSQC) that suppress the J_CH-dependence by modulating the polarization transfer delays of HSQC or, additionally, employ a pure-shift homodecoupling approach in the 1H dimension, such as Quantitative, Perfected and Pure Shifted HSQC (QUIPU-HSQC). To test the quantitative accuracy of these three methods, employing a 600 MHz NMR spectrometer equipped with a helium cooled cryoprobe, a Latin-square design that covered the physiological concentration ranges of 10 metabolites was used. The results show the suitability of all three methods for the quantification of highly abundant metabolites. However, the substantially increased residual water signal observed in QUIPU-HSQC spectra impeded the quantification of low abundant metabolites located near the residual water signal, thus limiting its utility in high-throughput metabolite fingerprinting studies.

Metabolomic profiling of aqueous humor from glaucoma patients - The metabolomics in surgical ophthalmological patients (MISO) study

Glaucoma is still a poorly understood disease with a clear need for new biomarkers to help in diagnosis and potentially offer new therapeutic targets. We aimed to determine if the metabolic profile of aqueous humor (AH) as determined by nuclear magnetic resonance (NMR) spectroscopy allows the distinction between primary open-angle glaucoma patients and control subjects, and to distinguish between high-tension (POAG) and normal-tension glaucoma (NTG). We analysed the AH of patients with POAG, NTG and control subjects (n = 30/group). ¹H NMR spectra were acquired using a 400 MHz spectrometer. Principle component analysis (PCA), machine learning algorithms and descriptive statistics were applied to analyse the metabolic variance between groups, identify the spectral regions, and hereby potential metabolites that can act as biomarkers for glaucoma. According to PCA, fourteen regions of the NMR spectra were significant in explaining the metabolic variance between the glaucoma and control groups, with no differences found between POAG and NTG groups. These regions were further used in building a classifier for separating glaucoma from control patients, which achieved an AUC of 0.93. Peak integration was performed on these regions and a statistical analysis, after false discovery rate correction and adjustment for the different perioperative topical drug regimen, revealed that five of them were significantly different between groups. The glaucoma group showed a higher content in regions typical for betaine and taurine, possibly linked to neuroprotective mechanisms, and also a higher content in regions that are typical for glutamate, which can indicate damaged neurons and oxidative stress. These results show how aqueous humor metabolomics based on NMR spectroscopy can distinguish glaucoma patients from controls with a high accuracy. Further studies are needed to validate these results in order to incorporate them in clinical practice.

Identifying unknown metabolites using NMR-based metabolic profiling techniques

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

2,3-Dihydroxyisovalerate production by Klebsiella pneumoniae

2,3-Dihydroxyisovalerate is an intermediate of valine and leucine biosynthesis pathway; however, no natural microorganism has been found yet that can accumulate this compound. Klebsiella pneumoniae is a useful bacterium that can be used as a workhorse for the production of a range of industrially desirable chemicals. Dihydroxy acid dehydratase, encoded by the ilvD gene, catalyzes the reaction of 2-ketoisovalerate formation from 2,3-dihydroxyisovalerate. In this study, an ilvD disrupted strain was constructed which resulted in the inability to synthesize 2-ketoisovalerate, yet accumulate 2,3-dihydroxyisovalerate in its culture broth. 2,3-Butanediol is the main metabolite of K. pneumoniae and its synthesis pathway and the branched-chain amino acid synthesis pathway share the same step of the α-acetolactate synthesis. By knocking out the budA gene, carbon flow into the branched-chain amino acid synthesis pathway was upregulated, which resulted in a distinct increase in 2,3-dihydroxyisovalerate levels. Lactic acid was identified as a by-product of the process and by blocking the lactic acid synthesis pathway, a further increase in 2,3-dihydroxyisovalerate levels was obtained. The culture parameters of 2,3-dihydroxyisovalerate fermentation were optimized, which include acidic pH and medium level oxygen supplementation to favor 2,3-dihydroxyisovalerate synthesis. At optimal conditions (pH 6.5, 400 rpm), 36.5 g/L of 2,3-dihydroxyisovalerate was produced in fed-batch fermentation over 45 h, with a conversion ratio of 0.49 mol/mol glucose. Thus, a biological route of 2,3-dihydroxyisovalerate production with high conversion ratio and final titer was developed, providing a basis for an industrial process. Key Points • A biological route of 2,3-dihydroxyisovalerate production was setup. • Disruption of budA causes 2,3-dihydroxuisovalerate accumulation in K. pneumoniae. • Disruption of ilvD prevents 2,3-dihydroxyisovalerate reuse by the cell. • 36.5 g/L of 2,3-dihydroxyisovalerate was obtained in fed-batch fermentation.

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.

NMR metabolomics identifies over 60 biomarkers associated with Type II Diabetes impairment in db/db mice

INTRODUCTION

The rapid expansion of Type 2 Diabetes (T2D), that currently affects 90% of people suffering from diabetes, urges us to develop a better understanding of the metabolic processes involved in the disease process in order to develop better therapies. The most commonly used model for T2D research is the db/db (BKS.Cg-Dock7 < m > +/+ Lepr < db >/J) mouse model. Yet, a systematic ¹H NMR based metabolomics characterisation of most tissues in this animal model has not been published. Here, we provide a systematic organ-specific metabolomics analysis of this widely employed model using NMR spectroscopy.

OBJECTIVES

The aim of this study was to characterise the metabolic modulations associated with T2D in db/db mice in 18 relevant biological matrices.

METHODS

High-resolution ¹H-NMR and 2D-NMR spectroscopy were applied to 18 biological matrices of 12 db/db mice (WT control n = 6, db/db = 6) aged 22 weeks, when diabetes is fully established.

RESULTS

61 metabolites associated with T2D were identified. Kidney, spleen, eye and plasma were the biological matrices carrying the largest metabolomics modulations observed in established T2D, based on the total number of metabolites that showed a statistical difference between the diabetic and control group in each tissue (16 in each case) and the strength of the O-PLS DA model for each tissue. Glucose and glutamate were the most commonly associated metabolites found significantly increased in nine biological matrices. Investigated sections where no increase of glucose was associated with T2D include all intestinal segments (i.e. duodenum, jejunum, ileum and colon). Microbial co-metabolites such as acetate and butyrate, used as carbon sources by the host, were identified in excess in the colonic tissues of diabetic individuals.

CONCLUSIONS

The metabolic biomarkers identified using ¹H NMR-based metabolomics will represent a useful resource to explore metabolic pathways involved in T2D in the db/db mouse model.

Metabolic Response in Rats following Electroacupuncture or Moxibustion Stimulation

Electroacupuncture and moxibustion are traditional Chinese medicine practices that exert therapeutic effects through stimulation of specific meridian acupoints. However, the biological basis of the therapies has been difficult to establish; thus the current practices still rely on ancient TCM references. Here, we used a rat model to study perturbations in cortex, liver, and stomach metabolome and plasma hormones following electroacupuncture or moxibustion treatment on either stomach meridian or gallbladder meridian acupoints. All treatment groups, regardless of meridian and mode of treatment, showed perturbation in cortex metabolome and increased phenylalanine, tyrosine, and branched-chain amino acids in liver. In addition, electroacupuncture was found to increase ATP in cortex, creatine, and dimethylglycine in stomach and GABA in liver. On the other hand, moxibustion increased plasma enkephalin concentration, as well as betaine and fumarate concentrations in stomach. Furthermore, we had observed meridian-specific changes including increased N-acetyl-aspartate in liver and 3-hydroxybutyrate in stomach for gallbladder meridian stimulation and increased noradrenaline concentration in blood plasma following stimulation on stomach meridian. In summary, the current findings may provide insight into the metabolic basis of electroacupuncture and moxibustion, which may contribute towards new application of acupoint stimulation.

Thanatometabolomics: introducing NMR-based metabolomics to identify metabolic biomarkers of the time of death

INTRODUCTION

Death is the permanent cessation of the critical functions of the organism as a whole. However, the shutdown of a complex biological organism does not abruptly terminate at time of death. New high-throughput technologies allow the systematic investigation of the biochemical modulations occurring after death. Recent genomics studies have demonstrated that genes remain active after death, triggering upregulation of some genes and initiating feedback loops. These genes were mostly involved in pathways related to immunity, inflammation and cancer. These genetic modulations suggest many biochemical events persist after death, which can be captured using a metabolomics approach.

OBJECTIVES

This proof of concept work aimed to determine whether NMR spectroscopy could identify metabolomics changes occurring after death, and characterise the nature of these metabolomics modulations.

METHODS

High-resolution 1H-NMR spectroscopy was applied to six biological matrices: heart, kidney, liver, spleen, skin and white adipose tissue of ten adult mice at three different type points.

RESULTS

Forty-three metabolites were associated with post mortem metabolomics modulations. Kidney, heart and spleen showed the highest metabolic perturbations. Conversely, skin and white adipose tissue were the least altered matrices. Early metabolic modulations were associated with energy metabolism and DNA synthesis, by contrast, late metabolomics modulations were associated with microbial metabolism.

CONCLUSIONS

NMR has proven potential to determine the time of death based on post-mortem metabolomics modulations. This could be useful in the context of transplants, forensic studies and as internal quality control in metabolomics studies. Further investigations are required to validate these findings in humans in order to determine which compounds robustly reflect post-mortem metabolic fluctuations to accurately determine the time of death.

Systematic Evaluation of Non-Uniform Sampling Parameters in the Targeted Analysis of Urine Metabolites by 1H,1H 2D NMR Spectroscopy

Non-uniform sampling (NUS) allows the accelerated acquisition of multidimensional NMR spectra. The aim of this contribution was the systematic evaluation of the impact of various quantitative NUS parameters on the accuracy and precision of 2D NMR measurements of urinary metabolites. Urine aliquots spiked with varying concentrations (15.6-500.0 µM) of tryptophan, tyrosine, glutamine, glutamic acid, lactic acid, and threonine, which can only be resolved fully by 2D NMR, were used to assess the influence of the sampling scheme, reconstruction algorithm, amount of omitted data points, and seed value on the quantitative performance of NUS in 1H,1H-TOCSY and 1H,1H-COSY45 NMR spectroscopy. Sinusoidal Poisson-gap sampling and a compressed sensing approach employing the iterative re-weighted least squares method for spectral reconstruction allowed a 50% reduction in measurement time while maintaining sufficient quantitative accuracy and precision for both types of homonuclear 2D NMR spectroscopy. Together with other advances in instrument design, such as state-of-the-art cryogenic probes, use of 2D NMR spectroscopy in large biomedical cohort studies seems feasible.

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

Urinary metabolic profiling by 1H NMR spectroscopy in patients with cirrhosis may discriminate overt but not covert hepatic encephalopathy

To date urinary metabolic profiling has been applied to define a specific metabolic fingerprint of hepatocellular carcinoma on a background of cirrhosis. Its utility for the stratification of other complications of cirrhosis, such as hepatic encephalopathy (HE), remains to be established. Urinary proton nuclear magnetic resonance (¹H-NMR) spectra were acquired and NMR data from 52 patients with cirrhosis (35 male; 17 female, median (range) age [60 (18-81) years]) and 17 controls were compared. A sub-set of 45 patients (33 male; 12 female, [60 (18-90) years, median model for end stage liver disease (MELD) score 11 (7-27)]) were fully characterised by West-Haven criteria, Psychometric Hepatic Encephalopathy Score (PHES) and electroencephalogram (EEG), and defined as overt HE (OHE, n = 21), covert HE (cHE, n = 7) or no HE (n = 17). Urinary proton nuclear magnetic resonance (¹H-NMR) spectra were analysed by partial-least-squares discriminant analysis (PLS-DA). The results showed good discrimination between patients with cirrhosis (n = 52) and healthy controls (n = 17) (R2X = 0.66, R2Y = 0.47, Q2Y = 0.31, sensitivity-60 %, specificity-100 %) as the cirrhosis group had higher 1-methylnicotinamide with lower hippurate, acetate, phenylacetylglycine and N-methyl nicotinic acid levels. While patients with OHE could be discriminated from those with no HE, with higher histidine, citrate and creatinine levels, the best models lack robust validity (R2X = 0.65, R2Y = 0.48, Q2Y = 0.12, sensitivity-100 %, specificity-64 %) with the sample size used. Urinary ¹H-NMR metabolic profiling did not discriminate patients with cHE from those without HE, nor discriminate subjects on the basis of PHES/EEG result or MELD score. In conclusion, patients with cirrhosis showed different urinary ¹H-NMR metabolic profiles compared to healthy controls and those with OHE may be distinguished from those with no HE although larger studies are required. However, urinary ¹H-NMR metabolic profiling did not discriminate patients with differing grades of HE or according to severity of underlying liver disease.

Quantification of Metabolites by NMR Spectroscopy in the Presence of Protein

The high reliability of NMR spectroscopy makes it an ideal tool for large-scale metabolomic studies. However, the complexity of biofluids and, in particular, the presence of macromolecules poses a significant challenge. Ultrafiltration and protein precipitation are established means of deproteinization and recovery of free or total metabolite content, but neither is ever complete. In addition, aside from cost and labor, all deproteinization methods constitute an additional source of experimental variation. The Carr-Purcell-Meiboom-Gill (CPMG) echo-train acquisition of NMR spectra obviates the need for prior deproteinization by attenuating signals from macromolecules, but concentration values of metabolites measured in blood plasma will not necessarily reflect total or free metabolite content. Here, in contrast to approaches that propose the determination of individual T₁ and T₂ relaxation times for the computation of correction factors, we demonstrate their determination by spike-in experiments with known amounts of metabolites in pooled samples of the matrix of interest to facilitate the measurement of total metabolite content. Provided that the protein content does not vary too much among individual samples, accurate quantitation of metabolites is feasible. Moreover, samples with significantly deviating protein content may be readily recognized by inclusion of a standard that shows moderate protein binding. It is also shown that urinary proteins when present in high concentrations may effect detection of common urinary metabolites prone to strong protein binding such as tryptophan.

Apoptotic signaling pathways induced by acute administration of branched-chain amino acids in an animal model of maple syrup urine disease

Maple Syrup Urine Disease (MSUD) is an inborn error of metabolism caused by a deficiency of the branched-chain α-keto acid dehydrogenase complex activity. This blockage leads to accumulation of the branched-chain amino acids leucine, isoleucine and valine, as well as their corresponding α-keto acids and α-hydroxy acids. The affected patients present severe neurological symptoms, such as coma and seizures, as well as edema and cerebral atrophy. Considering that the mechanisms of the neurological symptoms presented by MSUD patients are still poorly understood, in this study, protein levels of apoptotic factors are measured, such as Bcl-2, Bcl-xL, Bax, caspase-3 and -8 in hippocampus and cerebral cortex of rats submitted to acute administration of branched-chain amino acids during their development. The results in this study demonstrated that BCAA acute exposure during the early postnatal period did not significantly change Bcl-2, Bcl-xL, Bax and caspase-8 protein levels. However, the Bax/Bcl-2 ratio and procaspase-3 protein levels were decreased in hippocampus. On the other hand, acute administration of BCAA in 30-day-old rats increase in Bax/Bcl-2 ratio followed by an increased caspase-3 activity in cerebral cortex, whereas BCAA induces apoptosis in hippocampus through activation and cleavage of caspase-3 and -8 without changing the Bax/Bcl-2 ratio. In conclusion, the results suggest that apoptosis could be of pivotal importance in the developmental neurotoxic effects of BCAA. In addition, the current studies also suggest that multiple mechanisms may be involved in BCAA-induced apoptosis in the cerebral cortex and hippocampus.

NMR and MS Methods for Metabolomics

Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.

NMR-based metabolic characterization of chicken tissues and biofluids: a model for avian research

INTRODUCTION

Poultry is one of the most consumed meat in the world and its related industry is always looking for ways to improve animal welfare and productivity. It is therefore essential to understand the metabolic response of the chicken to new feed formulas, various supplements, infections and treatments.

OBJECTIVES

As a basis for future research investigating the impact of diet and infections on chicken's metabolism, we established a high-resolution proton nuclear magnetic resonance (NMR)-based metabolic atlas of the healthy chicken (Gallus gallus).

METHODS

Metabolic extractions were performed prior to 1H-NMR and 2D NMR spectra acquisition on twelve biological matrices: liver, kidney, spleen, plasma, egg yolk and white, colon, caecum, faecal water, ileum, pectoral muscle and brain of 6 chickens. Metabolic profiles were then exhaustively characterized.

RESULTS

Nearly 80 metabolites were identified. A cross-comparison of these matrices was performed to determine metabolic variations between and within each section and highlighted that only eight core metabolites were systematically found in every matrice.

CONCLUSION

This work constitutes a database for future NMR-based metabolomic investigations in relation to avian production and health.

NMR Chemical Shift Ranges of Urine Metabolites in Various Organic Solvents

Signal stability is essential for reliable multivariate data analysis. Urine samples show strong variance in signal positions due to inter patient differences. Here we study the exchange of the solvent of a defined urine matrix and how it affects signal and integral stability of the urinary metabolites by NMR spectroscopy. The exchange solvents were methanol, acetonitrile, dimethyl sulfoxide, chloroform, acetone, dichloromethane, and dimethyl formamide. Some of these solvents showed promising results with a single batch of urine. To evaluate further differences between urine samples, various acid, base, and salt solutions were added in a defined way mimicking to some extent inter human differences. Corresponding chemical shift changes were monitored.

Urinary Metabotyping of Hepatocellular Carcinoma in a UK Cohort Using Proton Nuclear Magnetic Resonance Spectroscopy

BACKGROUND

Discriminatory metabolic profiles have been described in urinary ¹H nuclear magnetic resonance (NMR) spectroscopy studies of African patients with hepatocellular carcinoma (HCC). This study aimed to assess similarities in a UK cohort, where there is a greater etiological diversity.

METHODS

Urine from cirrhosis and HCC patients was analyzed using a 600 MHz ¹H NMR system. Multivariate analysis and median group MR spectra comparison identified metabolite alterations between groups. Metabolite identification was achieved through literature reference and statistical total correlation spectroscopy. Diagnostic accuracy was compared to serum alpha-fetoprotein (AFP).

RESULTS

Of the 52 patients recruited, 13 samples from HCC and 25 from cirrhosis patients were selected. At 200 IU mL^-1, diagnostic sensitivity of AFP was 27%. Multivariate analysis of urinary spectra generated diagnostic models with a sensitivity/specificity of 53.6%/96%. p-Cresol sulfate (P = 0.04), creatinine (P = 0.03), citrate (P = 0.21) and hippurate (P = 0.52) were reduced in the HCC patients. Carnitine (P = 0.31) and formate (P = 0.44) were elevated.

CONCLUSION

Diagnostic sensitivity was lower than previous African studies, but still outperformed serum AFP. Reduced creatinine, citrate and hippurate and elevated carnitine are comparable with the African studies. p-Cresol sulfate alteration is a novel finding and may indicate an altered sulfonation capacity of the liver in patients with HCC.

Plasma metabolomic analysis of human hepatocellular carcinoma: Diagnostic and therapeutic study

Many hepatocellular carcinoma (HCC) patients suffer from late stages when diagnosed, leading to dismal prospects for cure. Improving the diagnosis and treatment of HCC remains a challenge. In this work, NMR-based metabolomic techniques were used to investigate the metabolic alterations of HCC patients from different pathological backgrounds. Metabolic improvement of clinical surgical treatments or transcatheter arterial chemoembolization (TACE) for recurrent or metastatic HCC was also evaluated. HCC was characterized by enhanced lipid metabolism and high consumption in response to liver injury. Expectedly, higher consumption of glucose and lactate production in TACE group confirmed that recurrent or metastatic HCC is more active in citric acid cycle and oxidative phosphorylation. However, TACE or surgical treatments did not immediately improve the metabolic profiles of HCC patients. Combining multivariate statistical analyses with univariate t-test, a series of characteristic metabolites were identified and served as biomarkers for discrimination of HCC patients in different pathological backgrounds. The relative metabolic pathway analyses help to get insight into the underlying biochemical mechanism and extend clinical relevance. Furthermore, algorithm of support vector classification was used to identify HCC and control subjects, and diagnostic sensitivity and specificity reached to 100% and 81.08% respectively by receiver operating characteristic analysis. It is concluded that NMR-based metabolomic analysis of plasma can provide a powerful approach to discover diagnostic and therapeutic biomarkers, and subsequently contribute to clinical disease management.

Clinical magnetic resonance spectroscopy of the central nervous system

Proton magnetic resonance spectroscopy (1H MRS) is a noninvasive imaging technique that can easily be added to the conventional magnetic resonance (MR) imaging sequences. Using MRS one can directly compare spectra from pathologic or abnormal tissue and normal tissue. Metabolic changes arising from pathology that can be visualized by MRS may not be apparent from anatomy that can be visualized by conventional MR imaging. In addition, metabolic changes may precede anatomic changes. Thus, MRS is used for diagnostics, to observe disease progression, monitor therapeutic treatments, and to understand the pathogenesis of diseases. MRS may have an important impact on patient management. The purpose of this chapter is to provide practical guidance in the clinical application of MRS of the brain. This chapter provides an overview of MRS-detectable metabolites and their significance. In addition some specific current clinical applications of MRS will be discussed, including brain tumors, inborn errors of metabolism, leukodystrophies, ischemia, epilepsy, and neurodegenerative diseases. The chapter concludes with technical considerations and challenges of clinical MRS.

NMR-Based Metabolic Snapshot from Minibronchoalveolar Lavage Fluid: An Approach To Unfold Human Respiratory Metabolomics

The utility of mini bronchoalveolar lavage (mBAL) and its applicability in metabolomics has not been explored in the field of human respiratory disease. mBAL, "an archetype" of the local lung environment, ensures a potent technique to get the snapshot of the epithelial lining fluid afflicted to human lung disorders. Characterization of the mBAL fluid has potential to help in elucidating the composition of the alveoli and airways in the diseased state, yielding diagnostic information on clinical applicability. In this study, one of the first attempts has been made to comprehensively assign and detect metabolites in mBAL fluid, extracted from human lungs, by the composite use of 800 MHz 1D and 2D NMR, J-resolved homonuclear spectroscopy, COSY, TOCSY, and heteronuclear HSQC correlation methods. A foremost all-inclusive sketch of the 50 metabolites has been corroborated and assigned, which can be a resourceful archive to further lung-directed metabolomics, prognosis, and diagnosis. Thus, NMR-based mBALF studies, as proposed in this article, will leverage many more prospective respiratory researches for routine clinical application and prove to be a viable approach to mirror the key predisposing factors contributing to the onset of lung disease.

Magnetic Resonance Spectroscopy: Principles and Techniques: Lessons for Clinicians

Magnetic resonance spectroscopy (MRS) provides a non-invasive 'window' on biochemical processes within the body. Its use is no longer restricted to the field of research, with applications in clinical practice increasingly common. MRS can be conducted at high magnetic field strengths (typically 11-14 T) on body fluids, cell extracts and tissue samples, with new developments in whole-body magnetic resonance imaging (MRI) allowing clinical MRS at the end of a standard MRI examination, obtaining functional information in addition to anatomical information. We discuss the background physics the busy clinician needs to know before considering using the technique as an investigative tool. Some potential applications of hepatic and cerebral MRS in chronic liver disease are also discussed.

Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit

Early diagnosis and patient stratification may improve sepsis outcome by a timely start of the proper specific treatment. We aimed to identify metabolomic biomarkers of sepsis in urine by ¹H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patients with severe sepsis or septic shock in the ICU for a ¹H NMR spectra acquisition. A supervised analysis was performed on the processed spectra, and a predictive model for prognosis (30-days mortality/survival) of sepsis was constructed using partial least-squares discriminant analysis (PLS-DA). In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score. Supervised multivariate analysis afforded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Negative prognosis patients presented higher values of ethanol, glucose and hippurate, and on the contrary, lower levels of methionine, glutamine, arginine and phenylalanine. These metabolites could be part of a composite biopattern of the human metabolic response to sepsis shock and its mortality in ICU patients. The internal cross-validation showed robustness of the metabolic predictive model obtained and a better predictive ability in comparison with SOFA values. Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage. A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.

Metabonomics and diagnostics

Metabonomic techniques have considerable potential in the field of clinical diagnostics, typifying the application of a translational research paradigm. Care must be taken at all stages to apply appropriate methodology with accurate patient selection and profiling, and rigorous data acquisition and handling, to ensure clinical validity.An ever-increasing number of publications in a wide range of diseases and diverse patient groups suggest a variety of potential clinical uses; prospective studies in large validation cohorts are required to bring metabonomics into routine clinical practice. In this chapter, the utility of metabonomics as a diagnostic tool will be discussed.

Monitoring the Response of the Human Urinary Metabolome to Brief Maximal Exercise by a Combination of RP-UPLC-MS and (1)H NMR Spectroscopy

The delineation of exercise biochemistry by utilizing metabolic fingerprinting has become an established strategy. We present a combined RP-UPLC-MS and (1)H NMR strategy, supplemented by photometric assays, to monitor the response of the human urinary metabolome to short maximal exercise. Seventeen male volunteers performed two identical sprint sessions on separate days, consisting of three 80 m maximal runs. Using univariate and multivariate analyses, we followed the fluctuation of 37 metabolites at 1, 1.5, and 2 h postexercise. 2-Hydroxyisovalerate, 2-hydroxybutyrate, 2-oxoisocaproate, 3-methyl-2-oxovalerate, 3-hydroxyisobutyrate, 2-oxoisovalerate, 3-hydroxybutyrate, 2-hydroxyisobutyrate, alanine, pyruvate, and fumarate increased 1 h postexercise and then returned toward baseline. Lactate and acetate were higher than baseline at 1 and 1.5 h. Hypoxanthine and inosine remained above baseline throughout the postexercise period. Urate decreased at 1 h and increased at 1.5 h before returning to baseline. Valine, isoleucine, succinate, citrate, trimethylamine, trimethylamine N-oxide, tyrosine, and formate decreased at 1 h and/or 1.5 h postexercise and then returned to baseline. Creatinine gradually decreased over the sampling period. Glycine, 4-aminohippurate, and hippurate remained below baseline throughout the postexercise period. Our findings show that even one-half minute of maximal exercise elicited major perturbations in human metabolism, several of which persisted for at least 2 h.

High-Resolution (1)H NMR Spectroscopy Discriminates Amniotic Fluid of Fetuses with Congenital Diaphragmatic Hernia from Healthy Controls

Lung hypoplasia in congenital diaphragmatic hernia (CDH) is a life-threatening birth defect. Severe cases can be offered tracheal occlusion to boost prenatal lung development, although defining those to benefit remains challenging. Metabonomics of (1)H NMR spectra collected from amniotic fluid (AF) can identify general changes in diseased versus healthy fetuses. AF embodies lung secretions and hence might contain pulmonary next to general markers of disease in CDH fetuses. AF from 81 healthy and 22 CDH fetuses was collected. NMR spectroscopy was performed at 400 MHz to compare AF from fetuses with CDH against controls. Several advanced feature extraction methods based on statistical tests that explore spectral variability, similarity, and dissimilarity were applied and compared. This resulted in the identification of 30 spectral regions, which accounted for 80% variability between CDH and controls. Combination with automated classification discriminates AF from CDH versus healthy fetuses with up to 92% accuracy. Within the identified spectral regions, isoleucine, leucine, valine, pyruvate, GABA, glutamate, glutamine, citrate, creatine, creatinine, taurine, and glucose were the most concentrated metabolites. As the metabolite pattern of AF changes with fetal development, we have excluded metabolites with a high age-related variability and repeated the analysis with 12 spectral regions, which has resulted in similar classification accuracy. From this analysis, it was possible to distinguish between AF from CDH fetuses versus healthy controls independent of gestational age.

Discrimination of human semen specimens by NMR data, sperm parameters, and statistical analysis

Human seminal fluid is a complex mixture of secretions originated from epididymis and the male accessory sex glands. It contains a variety of both inorganic and organic components, among which proteins are a major part of the high molecular-mass substances. In this study, 83 human seminal plasma samples were analyzed using a combined Nuclear Magnetic Resonance (NMR) Spectroscopy and Principal Component Analysis (PCA) approach to discriminate patients in relation to semen characteristics and/or conditions affecting the fertility status. Results showed a discrimination between patients with leukocytospermia and with the concomitant presence of varicocele/ex varicocele and leukocytospermia. Patients with testicular cancer, necrozoospermia, and azoospermia were separated from the other patient clusters. In addition, a differentiation of semen quality was also possible. This study represents to first use of sperm parameters together with NMR data as variables in the PCA analysis. Furthermore, this methodology allows the identification of the metabolites which play the most important role in identifying differences among human seminal plasma samples.

Data Normalization of (1)H NMR Metabolite Fingerprinting Data Sets in the Presence of Unbalanced Metabolite Regulation

Data normalization is an essential step in NMR-based metabolomics. Conducted properly, it improves data quality and removes unwanted biases. The choice of the appropriate normalization method is critical and depends on the inherent properties of the data set in question. In particular, the presence of unbalanced metabolic regulation, where the different specimens and cohorts under investigation do not contain approximately equal shares of up- and down-regulated features, may strongly influence data normalization. Here, we demonstrate the suitability of the Shapiro-Wilk test to detect such unbalanced regulation. Next, employing a Latin-square design consisting of eight metabolites spiked into a urine specimen at eight different known concentrations, we show that commonly used normalization and scaling methods fail to retrieve true metabolite concentrations in the presence of increasing amounts of glucose added to simulate unbalanced regulation. However, by learning the normalization parameters on a subset of nonregulated features only, Linear Baseline Normalization, Probabilistic Quotient Normalization, and Variance Stabilization Normalization were found to account well for different dilutions of the samples without distorting the true spike-in levels even in the presence of marked unbalanced metabolic regulation. Finally, the methods described were applied successfully to a real world example of unbalanced regulation, namely, a set of plasma specimens collected from patients with and without acute kidney injury after cardiac surgery with cardiopulmonary bypass use.

(1) H NMR Metabolic Profiling of Biofluids from Rats with Gastric Mucosal Lesion and Electroacupuncture Treatment

Gastric mucosal lesion (GML) is a common gastrointestinal disorder with multiple pathogenic mechanisms in clinical practice. In traditional Chinese medicine (TCM), electroacupuncture (EA) treatment has been proven as an effective therapy for GML, although the underlying healing mechanism is not yet clear. Here, we used proton nuclear magnetic resonance- (¹H NMR-) based metabolomic method to investigate the metabolic perturbation induced by GML and the therapeutic effect of EA treatment on stomach meridian (SM) acupoints. Clear metabolic differences were observed between GML and control groups, and related metabolic pathways were discussed by means of online metabolic network analysis toolbox. By comparing the endogenous metabolites from GML and GML-SM groups, the disturbed pathways were partly recovered towards healthy state via EA treated on SM acupoints. Further comparison of the metabolic variations induced by EA stimulated on SM and the control gallbladder meridian (GM) acupoints showed a quite similar metabolite composition except for increased phenylacetylglycine, 3,4-dihydroxymandelate, and meta-hydroxyphenylacetate and decreased N-methylnicotinamide in urine from rats with EA treated on SM acupoints. The current study showed the potential application of metabolomics in providing further insight into the molecular mechanism of acupuncture.

1H NMR-based metabolomics investigation of copper-laden rat: a model of Wilson's disease

BACKGROUND AND PURPOSE

Wilson's disease (WD), also known as hepatoleticular degeneration (HLD), is a rare autosomal recessive genetic disorder of copper metabolism, which causes copper to accumulate in body tissues. In this study, rats fed with copper-laden diet are used to render the clinical manifestations of WD, and their copper toxicity-induced organ lesions are studied. To investigate metabolic behaviors of 'decoppering' process, penicillamine (PA) was used for treating copper-laden rats as this chelating agent could eliminate excess copper through the urine. To date, there has been limited metabolomics study on WD, while metabolic impacts of copper accumulation and PA administration have yet to be established.

MATERIALS AND METHODS

A combination of 1HNMR spectroscopy and multivariate statistical analysis was applied to examine the metabolic profiles of the urine and blood serum samples collected from the copper-laden rat model of WD with PA treatment.

RESULTS

Copper accumulation in the copper-laden rats is associated with increased lactate, creatinine, valine and leucine, as well as decreased levels of glucose and taurine in the blood serum. There were also significant changes in p-hydroxyphenylacetate (p-HPA), creatinine, alpha-ketoglutarate (α-KG), dimethylamine, N-acetylglutamate (NAG), N-acetylglycoprotein (NAC) in the urine of these rats. Notably, the changes in p-HPA, glucose, lactate, taurine, valine, leucine, and NAG were found reversed following PA treatment. Nevertheless, there were no changes for dimethylamine, α-KG, and NAC as a result of the treatment. Compared with the controls, the concentrations of hippurate, formate, alanine, and lactate were changed when PA was applied and this is probably due to its side effect. A tool named SMPDB (Small Molecule Pathway Database) is introduced to identify the metabolic pathway influenced by the copper-laden diet.

CONCLUSION

The study has shown the potential application of NMR-based metabolomic analysis in providing further insights into the molecular mechanism underlying disorder due to WD.

Dominant components of the Thoroughbred metabolome characterised by (1) H-nuclear magnetic resonance spectroscopy: A metabolite atlas of common biofluids

REASONS FOR PERFORMING STUDY

Metabonomics is emerging as a powerful tool for disease screening and investigating mammalian metabolism. This study aims to create a metabolic framework by producing a preliminary reference guide for the normal equine metabolic milieu.

OBJECTIVES

To metabolically profile plasma, urine and faecal water from healthy racehorses using high resolution (1) H-nuclear magnetic resonance (NMR) spectroscopy and to provide a list of dominant metabolites present in each biofluid for the benefit of future research in this area.

STUDY DESIGN

This study was performed using 7 Thoroughbreds in race training at a single time point. Urine and faecal samples were collected noninvasively and plasma was obtained from samples taken for routine clinical chemistry purposes.

METHODS

Biofluids were analysed using (1) H-NMR spectroscopy. Metabolite assignment was achieved via a range of one- and 2-dimensional experiments.

RESULTS

A total of 102 metabolites were assigned across the 3 biological matrices. A core metabonome of 14 metabolites was ubiquitous across all biofluids. All biological matrices provided a unique window on different aspects of systematic metabolism. Urine was the most populated metabolite matrix with 65 identified metabolites, 39 of which were unique to this biological compartment. A number of these were related to gut microbial host cometabolism. Faecal samples were the most metabolically variable between animals; acetate was responsible for the majority (28%) of this variation. Short-chain fatty acids were the predominant features identified within this biofluid by (1) H-NMR spectroscopy.

CONCLUSIONS

Metabonomics provides a platform for investigating complex and dynamic interactions between the host and its consortium of gut microbes and has the potential to uncover markers for health and disease in a variety of biofluids. Inherent variation in faecal extracts along with the relative abundance of microbial-mammalian metabolites in urine and invasive nature of plasma sampling, infers that urine is the most appropriate biofluid for the purposes of metabonomic analysis.

NMR-based metabolomics approach to study the chronic toxicity of crude ricin from castor bean kernels on rats

Ricin, a large, water soluble toxic glycoprotein, is distributed majorly in the kernels of castor beans (the seeds of Ricinus communis L.) and has been used in traditional Chinese medicine (TCM) or other folk remedies throughout the world. The toxicity of crude ricin (CR) from castor bean kernels was investigated for the first time using an NMR-based metabolomic approach complemented with histopathological inspection and clinical chemistry. The chronic administration of CR could cause kidney and lung impairment, spleen and thymus dysfunction and diminished nutrient intake in rats. An orthogonal signal correction partial least-squares discriminant analysis (OSC-PLSDA) of metabolomic profiles of rat biofluids highlighted a number of metabolic disturbances induced by CR. Long-term CR treatment produced perturbations on energy metabolism, nitrogen metabolism, amino acid metabolism and kynurenine pathway, and evoked oxidative stress. These findings could explain well the CR induced nephrotoxicity and pulmonary toxicity, and provided several potential biomarkers for diagnostics of these toxicities. Such a (1)H NMR based metabolomics approach showed its ability to give a systematic and holistic view of the response of an organism to drugs and is suitable for dynamic studies on the toxicological effects of TCM.

NMR-Based Screening for Inborn Errors of Metabolism: Initial Results from a Study on Turkish Neonates

Approximately 1 in 400 neonates in Turkey is affected by inherited metabolic diseases. This high prevalence is at least in part due to consanguineous marriages. Standard screening in Turkey now covers only three metabolic diseases (phenylketonuria, congenital hypothyroidism, and biotinidase deficiency). Once symptoms have developed, tandem-MS can be used, although this currently covers only up to 40 metabolites. NMR potentially offers a rapid and versatile alternative.We conducted a multi-center clinical study in 14 clinical centers in Turkey. Urine samples from 989 neonates were collected and investigated by using NMR spectroscopy in two different laboratories. The primary objective of the present study was to explore the range of variation of concentration and chemical shifts of specific metabolites without clinically relevant findings that can be detected in the urine of Turkish neonates. The secondary objective was the integration of the results from a healthy reference population of neonates into an NMR database, for routine and completely automatic screening of congenital metabolic diseases.Both targeted and untargeted analyses were performed on the data. Targeted analysis was aimed at 65 metabolites. Limits of detection and quantitation were determined by generating urine spectra, in which known concentrations of the analytes were added electronically as well as by real spiking. Untargeted analysis involved analysis of the whole spectrum for abnormal features, using statistical procedures, including principal component analysis. Outliers were eliminated by model building. Untargeted analysis was used to detect known and unknown compounds and jaundice, proteinuria, and acidemia. The results will be used to establish a database to detect pathological concentration ranges and for routine screening.

1H NMR-based metabonomic analysis of serum and urine in a nonhuman primate model of diabetic nephropathy

Diabetic nephropathy (DN) is a serious metabolic disease, and comprehensive understanding of its complex mechanism will help in preventing the onset and progression of DN. To reveal the systemic metabolic changes associated with renal injury, we performed 1H NMR-based metabonomic and multivariate analyses to analyze serum and urine obtained from a nonhuman primate model of DN. Our results indicated that DN monkeys exhibited a distinct metabolic profile, including higher levels of VLDL/LDL, lipids, unsaturated lipids, uric acid, allantoin, fumarate and hippurate, as well as lower levels of HDL, alanine, glutamate, pyruvate, formate, tyrosine, histidine and NAD+. The disturbed metabolic pathways were further identified, including NAD+ metabolism, purine metabolism, oxidative stress, lipid metabolism, and renal tubular reabsorption. This study highlights that NMR-based metabonomics provides insight into the underlying pathways in the pathogenesis and progression of DN at the metabolic level.

Urine NMR metabolomics analysis of breastfeeding biomarkers during and after pregnancy in a large prospective cohort study

BACKGROUND

Modern metabolomic profiling has not yet been applied to human breastfeeding research. A common reason for breastfeeding cessation is perceived insufficient milk production. We investigated broad biochemical profiles in maternal urine collected during and after pregnancy to identify biomarkers related to reduced reported breastfeeding.

METHODS

Fasting urine was collected at three consultations (visit V1: gestational week 8-20; V2: week 28 ± 2; V3: 10-16 weeks postpartum) in the STORK Groruddalen program, a prospective, multiethnic cohort study of gestational diabetes involving healthy, pregnant women in Oslo, Norway, and analyzed using NMR spectroscopy. Breastfeeding at V3 was recorded in three categories: Exclusively breastfeeding (n = 326), partially breastfeeding (n = 156) and formula feeding (n = 67).

RESULTS

Five metabolites were relevant to breastfeeding. Lactose was detected at V1 and increased to 0.1 mM/mM creatinine at V2. Postpartum excretion at V3 was significantly higher in exclusively breastfeeding women than partially or non-breastfeeding (median = 0.29, 0.23 and 0.04 mM/mM creatine, respectively; ANOVA p-value = 2e-70). Glycine excretion at V3 (0.12, 0.10 and 0.06, respectively; p = 2e-5) and at V2 were associated with breastfeeding (0.34, 0.33 and 0.26, respectively; p = 4e-5). Creatine and two unidentified substances also correlated with breastfeeding. NMR metabolomics found no other metabolites differing between categories during pregnancy (V1, V2), and did not predict individual breastfeeding postpartum (V3).

CONCLUSION

Decreased glycine excretion at V2 may indicate difficulties meeting the metabolic demands of the growing fetus, but urine profiles contained otherwise little indication of early adaptations during pregnancy towards reduced biological potential to breastfeed.

¹H NMR-based metabolic profiling of human rectal cancer tissue

Background

Rectal cancer is one of the most prevalent tumor types. Understanding the metabolic profile of rectal cancer is important for developing therapeutic approaches and molecular diagnosis.

Methods

Here, we report a metabonomics profiling of tissue samples on a large cohort of human rectal cancer subjects (n = 127) and normal controls (n = 43) using ¹H nuclear magnetic resonance (¹H NMR) based metabonomics assay, which is a highly sensitive and non-destructive method for the biomarker identification in biological systems. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA) were applied to analyze the ¹H-NMR profiling data to identify the distinguishing metabolites of rectal cancer.

Results

Excellent separation was obtained and distinguishing metabolites were observed among the different stages of rectal cancer tissues (stage I = 35; stage II = 37; stage III = 37 and stage IV = 18) and normal controls. A total of 38 differential metabolites were identified, 16 of which were closely correlated with the stage of rectal cancer. The up-regulation of 10 metabolites, including lactate, threonine, acetate, glutathione, uracil, succinate, serine, formate, lysine and tyrosine, were detected in the cancer tissues. On the other hand, 6 metabolites, including myo-inositol, taurine, phosphocreatine, creatine, betaine and dimethylglycine were decreased in cancer tissues. These modified metabolites revealed disturbance of energy, amino acids, ketone body and choline metabolism, which may be correlated with the progression of human rectal cancer.

Conclusion

Our findings firstly identify the distinguishing metabolites in different stages of rectal cancer tissues, indicating possibility of the attribution of metabolites disturbance to the progression of rectal cancer. The altered metabolites may be as potential biomarkers, which would provide a promising molecular diagnostic approach for clinical diagnosis of human rectal cancer. The role and underlying mechanism of metabolites in rectal cancer progression are worth being further investigated.

VIZR--an automated chemometric technique for metabolic profiling

A chemometric technique, visual interpretation of z-score ratios (VIZR), written in the open source code R, has been developed to identify metabolic differences between individual biosamples and a control group. To demonstrate the capabilities of VIZR, 49 urine samples were collected from healthy volunteers: 41 samples were collected randomly following a normal dietary routine and 7 test samples were collected after dietary supplementation with either ibuprofen or alcoholic beverages. An eighth test sample was prepared by 50% dilution of a control sample. Sample analysis was conducted by (1)H nuclear magnetic resonance (NMR) spectroscopy and the collected data were subjected to VIZR analysis, which successfully discriminated each of the 8 test samples from the 41 control samples. In addition, VIZR analysis revealed the NMR spectral regions responsible for the disparity between the individual test samples and the control group. The self-normalizing nature of the VIZR calculation provides a robust analysis independent of dilution effects, which is especially important in urine analyses. Potential applications of VIZR include high-throughput data analysis for toxicological profiling, disease diagnosis, and biomarker identification in any type of biosample for which a control dataset can be established. Although demonstrated herein for the statistical analysis of (1)H NMR data, the VIZR program is platform independent and could be applied to digitized metabolic datasets acquired using other techniques including hyphenated mass spectrometry measurements.

Early metabolic adaptation in C57BL/6 mice resistant to high fat diet induced weight gain involves an activation of mitochondrial oxidative pathways

We investigated the short-term (7 days) and long-term (60 days) metabolic effect of high fat diet induced obesity (DIO) and weight gain in isogenic C57BL/6 mice and examined the specific metabolic differentiation between mice that were either strong-responders (SR), or non-responders (NR) to weight gain. Mice (n = 80) were fed a standard chow diet for 7 days prior to randomization into a high-fat (HF) (n = 56) or a low-fat (LF) (n = 24) diet group. The (1)H NMR urinary metabolic profiles of LF and HF mice were recorded 7 and 60 days after the diet switch. On the basis of the body weight gain (BWG) distribution of HF group, we identified NR mice (n = 10) and SR mice (n = 14) to DIO. Compared with LF, HF feeding increased urinary excretion of glycine conjugates of β-oxidation intermediate (hexanoylglycine), branched chain amino acid (BCAA) catabolism intermediates (isovalerylglycine, α-keto-β-methylvalerate and α-ketoisovalerate) and end-products of nicotinamide adenine dinucleotide (NAD) metabolism (N1-methyl-2-pyridone-5-carboxamide, N1-methyl-4-pyridone-3-carboxamide) suggesting up-regulation of mitochondrial oxidative pathways. In the HF group, NR mice excreted relatively more hexanoylglycine, isovalerylglycine, and fewer tricarboxylic acid (TCA) cycle intermediate (succinate) in comparison to SR mice. Thus, subtle regulation of ketogenic pathways in DIO may alleviate the saturation of the TCA cycle and mitochondrial oxidative metabolism.

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.

Fast and accurate quantitative metabolic profiling of body fluids by nonlinear sampling of 1H–13C two-dimensional nuclear magnetic resonance spectroscopy

Two-dimensional (2D) nuclear magnetic resonance (NMR) methods have shown to be an excellent analytical tool for the identification and characterization of statistically relevant changes in low-abundance metabolites in body fluid. The advantage of 2D NMR in terms of minimized ambiguities in peak assignment, aided in metabolite identifications and comprehensive metabolic profiling comes with the cost of increased NMR data collection time; making it inconvenient choice for routine metabolic profiling. We present here a method for the reduction in NMR data collection time of 2D (1)H-(13)C NMR spectroscopy for the purpose of quantitative metabolic profiling. Our method combines three techniques; which are nonlinear sampling (NLS), forward maximum (FM) entropy reconstruction, and J-compensated quantitative heteronuclear single quantum (HSQC) (1)H-(13)C NMR spectra. We report here that approximately 22-fold reduction in 2D NMR data collection time for the body fluid samples can be achieved by this method, without any compromise in quantitative information recovery of various low abundance metabolites. The method has been demonstrated in standard mixture solution, native, and lyophilized human urine samples. Our proposed method has potential to make quantitative metabolic profiling by 2D NMR as a routine method for various metabonomic studies.