Revealing the metabonomic variation of EC using 1H-NMR spectroscopy and its association with the clinicopathological characteristics

In this study, (1)H NMR-based metabonomics has been applied to investigate esophageal cancer metabolic signatures in plasma and urine, purpose of assessing the diagnostic potential of this approach and gaining novel insights into esophageal cancer metabolism and systemic effects. Plasma and urine samples from esophageal cancer patients (n = 108) and a control healthy group (n = 40) were analyzed by Nuclear Magnetic Resonance (NMR) spectroscopy (600 MHz), and their spectral profiles subjected to Orthogonal Projections to Latent Structures (OPLS-DA) for multivariate statistics. Potential metabolic biomarkers were identified using data base comparisons used for examining the significance of metabolites. Compared to healthy controls, esophageal cancer plasma had higher levels of dimethylamine, α-glucose, β-glucose, citric acid, together with lower levels of Leucine, alanine, isoleucine, valine, glycoprotein, lactate, acetone, acetate, choline, isobutyrate, unsaturated lipid, VLDL, LDL, 1-methylhistidine; Compared to healthy controls, esophageal cancer urine had higher levels of Mannitol, glutamate, γ-propalanine, phenylalanine, acetate, allantoin, pyruvate, tyrosine, β-glucose and guinolinate, together with lower levels of N-acetylcysteine, valine, dihydrothymine, hippurate, methylguanidine, 1-methylnicotin- amide and Citric acid; Very good discrimination between cancer and control groups was achieved by multivariate modeling of plasma and urinary profiles. (1)H NMR-based metabolite profiling analysis was shown to be an effective approach to differentiating between patients with EC and healthy subjects. Good sensitivity and selectivity were shown by using the metabolite markers discovered to predict the classification of samples from the healthy control group and the patients with the disease. Plasma and urine metabolic profiling may have potential for early diagnosis of EC and may enhance our understanding of its mechanisms.     

Metabonomic Profiling of Serum and Urine by 1H NMR-Based Spectroscopy Discriminates Patients with Chronic Obstructive Pulmonary Disease and Healthy Individuals

Chronic obstructive pulmonary disease (COPD) has seriously impacted the health of individuals and populations. In this study, proton nuclear magnetic resonance (¹H NMR)-based metabonomics combined with multivariate pattern recognition analysis was applied to investigate the metabolic signatures of patients with COPD. Serum and urine samples were collected from COPD patients (n = 32) and healthy controls (n = 21), respectively. Samples were analyzed by high resolution ¹H NMR (600 MHz), and the obtained spectral profiles were then subjected to multivariate data analysis. Consistent metabolic differences have been found in serum as well as in urine samples from COPD patients and healthy controls. Compared to healthy controls, COPD patients displayed decreased lipoprotein and amino acids, including branched-chain amino acids (BCAAs), and increased glycerolphosphocholine in serum. Moreover, metabolic differences in urine were more significant than in serum. Decreased urinary 1-methylnicotinamide, creatinine and lactate have been discovered in COPD patients in comparison with healthy controls. Conversely, acetate, ketone bodies, carnosine, m-hydroxyphenylacetate, phenylacetyglycine, pyruvate and α-ketoglutarate exhibited enhanced expression levels in COPD patients relative to healthy subjects. Our results illustrate the potential application of NMR-based metabonomics in early diagnosis and understanding the mechanisms of COPD.     

A metabonomic approach to evaluate COPD in a model of cigarette smoke exposure in mice

This study aims to develop an NMR-based metabonomic approach to diagnostic evaluation of Chronic Obstructive Pulmonary Disease (COPD) in a model of smoke exposure in mice. The current study compared the effects of acute and chronic smoke exposures performed on three strains of mice susceptible to develop COPD and two strains resistant to this disease. Intact lung tissue was examined using high-resolution magic angle spinning (HR-MAS). Six chemical shifts of the ¹H NMR spectrum including taurine, glutathione, phosphoryl choline and glycero-phosphocholine, were selected to ensure a high discrimination between control and acute exposed mice in the susceptible strain and, simultaneously, a low discrimination in the resistant strains. Using these metabolites, a chronic predictive model was developed and validated. The multivariate analysis provides a 100% true classification for sick mice and 78% for control animals. This pioneering study demonstrates that the NMR-based metabonomic analysis of intact lung tissue is a potential technique for the detection of COPD biomarkers and for the diagnosis of COPD in a mouse model of COPD. It might boost the study of tobacco smoke effects on other non- or less-invasive samples.     

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma

In this work, the variations in the metabolic profile of blood plasma from lung cancer patients and healthy controls were investigated through NMR-based metabonomics, to assess the potential of this approach for lung cancer screening and diagnosis. PLS-DA modeling of CPMG spectra from plasma, subjected to Monte Carlo Cross Validation, allowed cancer patients to be discriminated from controls with sensitivity and specificity levels of about 90%. Relatively lower HDL and higher VLDL + LDL in the patients' plasma, together with increased lactate and pyruvate and decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), and methanol, could be detected. These changes were found to be present at initial disease stages and could be related to known cancer biochemical hallmarks, such as enhanced glycolysis, glutaminolysis, and gluconeogenesis, together with suppressed Krebs cycle and reduced lipid catabolism, thus supporting the hypothesis of a systemic metabolic signature for lung cancer. Despite the possible confounding influence of age, smoking habits, and other uncontrolled factors, these results indicate that NMR-based metabonomics of blood plasma can be useful as a screening tool to identify suspicious cases for subsequent, more specific radiological tests, thus contributing to improved disease management.     

A 1H NMR-Based Metabonomic Investigation of Time-Related Metabolic Trajectories of the Plasma,Urine and Liver Extracts of Hyperlipidemic Hamsters

The hamster has been previously found to be a suitable model to study the changes associated with diet-induced hyperlipidemia in humans. Traditionally, studies of hyperlipidemia utilize serum- or plasma-based biochemical assays and histopathological evaluation. However, unbiased metabonomic technologies have the potential to identify novel biomarkers of disease. Thus, to obtain a better understanding of the progression of hyperlipidemia and discover potential biomarkers, we have used a proton nuclear magnetic resonance spectroscopy (¹H-NMR)-based metabonomics approach to study the metabolic changes occurring in the plasma, urine and liver extracts of hamsters fed a high-fat/high-cholesterol diet. Samples were collected at different time points during the progression of hyperlipidemia, and individual proton NMR spectra were visually and statistically assessed using two multivariate analyses (MVA): principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Using the commercial software package Chenomx NMR suite, 40 endogenous metabolites in the plasma, 80 in the urine and 60 in the water-soluble fraction of liver extracts were quantified. NMR analysis of all samples showed a time-dependent transition from a physiological to a pathophysiological state during the progression of hyperlipidemia. Analysis of the identified biomarkers of hyperlipidemia suggests that significant perturbations of lipid and amino acid metabolism, as well as inflammation, oxidative stress and changes in gut microbiota metabolites, occurred following cholesterol overloading. The results of this study substantially broaden the metabonomic coverage of hyperlipidemia, enhance our understanding of the mechanism of hyperlipidemia and demonstrate the effectiveness of the NMR-based metabonomics approach to study a complex disease.     

A novel R-package graphic user interface for the analysis of metabonomic profiles

Background  

Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries.     

An effective assessment of simvastatin-induced toxicity with NMR-based metabonomics approach

Background

Simvastatin, which is used to control elevated cholesterol levels, is one of the most widely prescribed drugs. However, a daily excessive dose can induce drug-toxicity, especially in muscle and liver. Current markers for toxicity reflect mostly the late stages of tissue damage; thus, more efficient methods of toxicity evaluation are desired.

Methodology/Principal Findings

As a new way to evaluate toxicity, we performed NMR-based metabonomics analysis of urine samples. Compared to conventional markers, such as AST, ALT, and CK, the urine metabolic profile provided clearer distinction between the pre- and post-treatment groups treated with toxic levels of simvastatin. Through multivariate statistical analysis, we identified marker metabolites associated with the toxicity. Importantly, we observed that the treatment group could be further categorized into two subgroups based on the NMR profiles: weak toxicity (WT) and high toxicity (HT). The distinction between these two groups was confirmed by the enzyme values and histopathological exams. Time-dependent studies showed that the toxicity at 10 days could be reliably predicted from the metabolic profiles at 6 days.

Conclusions/Significance

This metabonomics approach may provide a non-invasive and effective way to evaluate the simvastatin-induced toxicity in a manner that can complement current measures. The approach is expected to find broader application in other drug-induced toxicity assessments.     

Automated SPE-RP-HPLC fractionation of biofluids combined to off-line NMR spectroscopy for biomarker identification in metabonomics

NMR-based metabonomics is a valuable and straightforward approach to measuring hundreds of metabolites in complex biofluids. However, metabolite identification is sometimes limited by overlapped signals in NMR spectra. We describe a new methodology using an automated hyphenation of solid phase extraction (SPE) with RP-HPLC combined to NMR spectroscopy, which allowed identification of 72 metabolites of various molecular classes in human urine. This methodology was also successfully applied to the fractionation of a cat urine sample to aid identification of aromatic compounds and felinine. The SPE-RP-HPLC method appears to be a reliable tool to support biomarker discovery in metabonomic studies.     

Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting

We have recently reported the construction of an nuclear magnetic resonance （NMR）-based metabonomics study platform, Automics. To examine the application of Automics in transgenic plants, we performed metabolic fingerprinting analysis, i.e., 1H NMR spectroscopy and multivariate analysis, on wild-type and transgenic Arabidopsis. We found that it was possible to distinguish wild-type from four transgenic plants by PLS-DA following application of orthogonal signal correction （OSC）. Scores plot following OSC clearly demonstrates significant variation between the transgenic and non-transgenic groups, suggesting that the metabolic changes among wild-type and transgenic lines are possibly associated with transgenic event, We also found that the major contributing metabolites were some specific amino acids （i.e., threonine and alanine）, which could correspond to the insertion of the selective marker BAR gene in the transgenic plants. Our data suggests that NMR-based metabonomics is an efficient method to distinguish fingerprinting difference between wild-type and transgenic plants, and can potentially be applied in the bio-safety assessment of transgenic plants.     

Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting

We have recently reported the construction of an nuclear magnetic resonance (NMR)-based metabonomics study platform, Automics.To examine the application of Automics in transgenic plants, we performed metabolic fingerprinting analysis, i.e., 1H NMR spectroscopy and multivariate analysis, on wild-type and transgenic Arabidopsis. We found that it was possible to distinguish wild-type from four transgenic plants by PLS-DA following application of orthogonal signal correction (OSC). Scores plot following OSC clearly demonstrates significant variation between the transgenic and non-transgenic groups, suggesting that the metabolic changes among wild-type and transgenic lines are possibly associated with transgenic event. We also found that the major contributing metabolites were some specific amino acids (i.e., threonine and alanine), which could correspond to the insertion of the selective marker BAR gene in the transgenic plants. Our data suggests that NMR-based metabonomics is an efficient method to distinguish fingerprinting difference between wild-type and transgenic plants, and can potentially be applied in the bio-safety assessment of transgenic plants.     

The cardiovascular risk of healthy individuals studied by NMR metabonomics of plasma samples

The identification and the present wide acceptance of cardiovascular risk factors such as age, sex, hypertension, hyperlipidemia, smoking, obesity, diabetes, and physical inactivity have led to dramatic reductions in cardiovascular morbidity and mortality. However, novel risk predictors present opportunities to identify more patients at risk and to more accurately define the biochemical signature of that risk. In this paper, we present a comprehensive metabonomic analysis of 864 plasma samples from healthy volunteers, through Nuclear Magnetic Resonance (NMR) and multivariate statistical analysis (regression and classification). We have found that subjects that are classified as at high or at low risk using the common clinical markers can also be discriminated using NMR metabonomics. This discrimination is not only due to common markers (such as total cholesterol, triglycerides, LDL, HDL), but also to (p < 0.05 after Bonferroni correction) other metabolites (e.g., 3-hydroxybutyrate, α-ketoglutarate, threonine, dimethylglycine) previously not associated with cardiovascular diseases.     

Bladder cancer determination via two urinary metabolites: a biomarker pattern approach

The purpose of this study was to use metabonomic profiling to identify a potential specific biomarker pattern in urine as a noninvasive bladder cancer (BC) detection strategy. A liquid chromatography-mass spectrometry based method, which utilized both reversed phase liquid chromatography and hydrophilic interaction chromatography separations, was performed, followed by multivariate data analysis to discriminate the global urine profiles of 27 BC patients and 32 healthy controls. Data from both columns were combined, and this combination proved to be effective and reliable for partial least squares-discriminant analysis. Following a critical selection criterion, several metabolites showing significant differences in expression levels were detected. Receiver operating characteristic analysis was used for the evaluation of potential biomarkers. Carnitine C9:1 and component I, were combined as a biomarker pattern, with a sensitivity and specificity up to 92.6% and 96.9%, respectively, for all patients and 90.5% and 96.9%, respectively for low-grade BC patients. Metabolic pathways of component I and carnitine C9:1 are discussed. These results indicate that metabonomics is a practicable tool for BC diagnosis given its high efficacy and economization. The combined biomarker pattern showed better performance than single metabolite in discriminating bladder cancer patients, especially low-grade BC patients, from healthy controls.     

Prediction of variability in CYP3A4 induction using a combined 1H NMR metabonomics and targeted UPLC-MS approach

The activity of Cytochrome P450 3A4 (CYP3A4) enzyme is associated with many adverse or poor therapeutic responses to drugs. We used (1)H NMR-based metabonomics to identify a metabolic signature associated with variation in induced CYP3A4 activity. A total of 301 female twins, aged 45--84, participated in this study. Each volunteer was administered a potent inducer of CYP3A4 (St. John's Wort) for 14 days and the activity of CYP3A4 was quantified through the metabolism of the exogenously administered probe drug quinine sulfate (300 mg). Pre- and postintervention fasting urine samples were used to obtain metabolite profiles, using (1)H NMR spectroscopy, and were analyzed using UPLC--MS to obtain a marker for CYP3A4 induction, via the ratio of 3-hydroxyquinine to quinine (3OH-Q:Q). Multiple linear regression was used to build a predictive model for 3OH-Q:Q values based on the preintervention metabolite profiles. A combination of seven metabolites and seven covariates showed a strong (r = 0.62) relationship with log(3OH-Q:Q). This regression model demonstrated significant (p < 0.00001) predictive ability when applied to an independent validation set. Our results highlight the promise of metabonomics for predicting CYP3A4-mediated drug response.     

The mechanism of galactosamine toxicity revisited; a metabonomic study

1H NMR spectroscopy was used to investigate the metabolic effects of the hepatotoxin galactosamine (galN) and the mechanism by which glycine protects against such toxicity. Rats were acclimatized to a 0 or 5% glycine diet for 6 days and subsequently administered vehicle, galN (500 mg/kg), glycine (5% via the diet), or both galN and glycine. Urine was collected over 12 days prior to administration of galN and for 24 hours thereafter. Serum and liver tissue were sampled on termination, 24 hours post-dosing. The metabolic profiles of biofluids and tissues were determined using high-field 1H NMR spectroscopy. Orthogonal-projection to latent structures discriminant analysis (O-PLS-DA) was applied to model the spectral data and enabled the hepatic, urinary, and serum metabolites that discriminated between control and treated animals to be determined. Histopathological data and clinical chemistry measurements confirmed the protective effect of glycine. The level of N-acetylglucosamine (glcNAc) in the post-dose urine was found to correlate strongly with the degree of galN-induced liver damage, and the urinary level of glcNAc was not significantly elevated in rats treated with both galN and glycine. Treatment with glycine alone was found to significantly increase hepatic levels of uridine, UDP-glucose, and UDP-galactose, and in view of the known effects of galactosamine, this suggests that the protective role of glycine against galN toxicity might be mediated by changes in the uridine nucleotide pool rather than by preventing Kupffer cell activation. Thus, we present a novel hypothesis: that administration of glycine increases the hepatic uridine nucleotide pool which counteracts the galN-induced depletion of these pools and facilitates complete metabolism of galN. These novel data highlight the applicability of NMR-based metabonomics in elucidating multicompartmental metabolic consequences of toxicity and toxic salvage.     

NMR-based metabonomics: a useful platform of oncology research

Cancer threatens human health, thus research focusing on oncology has great significance. Metabonomics is the global quantitative assessment of the dynamic metabolic response of a biological system to some exogenous or genetic pathophysiological perturbation. The metabolites are detected in tissues or fluids by various analytical methods, such as nuclear magnetic resonance (NMR) and mass spectroscopy. Metabonomics, as a tool, can provide a link between the laboratory and clinic. NMR-based metabonomics offers a useful tool to understand tumour biochemistry and may also has some potentials for tumour diagnosis and prognosis, even when some other disease processes are present. Here, we review NMR-based metabonomics principles and their applications in oncology research.     

Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles

Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields, including work in cardiovascular research and drug toxicology. In this study, metabonomics method was employed to the diagnosis of Type 2 diabetes mellitus (DM2) based on serum lipid metabolites. The results suggested that serum fatty acid profiles determined by capillary gas chromatography combined with pattern recognition analysis of the data might provide an effective approach to the discrimination of Type 2 diabetic patients from healthy controls. And the applications of pattern recognition methods have improved the sensitivity and specificity greatly.     

Metabonomics study of transgenic Bacillus thuringiensis rice (T2A-1) meal in a 90-day dietary toxicity study in rats

Rice is one of the most important staple foods in the world. The Cry2A gene was inserted into the rice genome to help the plant combat insects. As the unintended effects of the genetically modified (GM) organisms are the most important barriers to the promotion of GM organisms, we have carried out a useful exploration to establish a new in vivo evaluation model for genetically modified foods by metabonomics methods. In this study, the rats were fed for 90 days with the GM and NON-GM rice diets. The changes in metabolites of the urine were detected using (1)H-NMR. The metabonomics were analyzed to see whether the GM rice can induce the metabolite changes in the rats' urine when compared with the NON-GM rice group. The multivariate analysis and ANOVA were used to determine the differences and the significance of differences respectively, and eventually we concluded that these differences did not have a biological significance. The conclusion of the metabonomics was comparable with that from the traditional method. As a non-invasive and dynamic monitoring method, metabonomics will be a new way of assessing the food safety of GM foods.     

NMR-based metabolomics study of canine bladder cancer

Bladder cancer is one of the leading lethal cancers worldwide. With the high risk of recurrence for bladder cancer following the initial diagnoses, lifelong monitoring of patients is necessary. The lack of adequate sensitivity and specificity of current noninvasive monitoring approaches including urine cytology, other urine tests, and imaging, underlines the importance of studies that focus on the detection of more reliable biomarkers for this cancer. The emerging area of metabolomics, which deals with the analysis of a large number of small molecules in a single step, promises immense potential for discovering metabolite markers for screening and monitoring treatment response and recurrence in patients with bladder cancer. Since naturally-occurring canine transitional cell carcinoma of the urinary bladder is very similar to human invasive bladder cancer, spontaneous canine transitional cell carcinoma has been applied as a relevant animal model of human invasive transitional cell carcinoma. In this study, we have focused on profiling the metabolites in urine from dogs with transitional cell carcinoma and healthy control dogs combining nuclear magnetic resonance spectroscopy and statistical analysis methods. ¹H NMR-based metabolite profiling analysis was shown to be an effective approach for differentiating samples from dogs with transitional cell carcinoma and healthy controls based on a partial least square-discriminant analysis of the NMR spectra. In addition, there were significant differences in the levels of six individual metabolites between samples from dogs with transitional cell carcinoma and the control group based on the Student's t-test. These metabolites were selected to build a separate partial least square‐discriminant analysis model that was then used to test the classification accuracy. The result showed good classification between transitional cell carcinoma and control groups with the area under the receiver operating characteristic curve of 0.85. The sensitivity and specificity of the model were 86% and 78%, respectively. These results suggest that urine metabolic profiling may have potential for early detection of bladder cancer and of bladder cancer recurrence following treatment, and may enhance our understanding of the mechanisms involved.     

Recommended strategies for spectral processing and post-processing of 1D <Superscript>1</Superscript>H-NMR data of biofluids with a particular focus on urine

¹H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.