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.     

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

In this study, 1H NMR-based metabonomics has been applied, for the first time to our knowledge, to investigate lung cancer metabolic signatures in urine, aiming at assessing the diagnostic potential of this approach and gaining novel insights into lung cancer metabolism and systemic effects. Urine samples from lung cancer patients (n = 71) and a control healthy group (n = 54) were analyzed by high resolution 1H NMR (500 MHz), and their spectral profiles subjected to multivariate statistics, namely, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Projections to Latent Structures (OPLS)-DA. Very good discrimination between cancer and control groups was achieved by multivariate modeling of urinary profiles. By Monte Carlo Cross Validation, the classification model showed 93% sensitivity, 94% specificity and an overall classification rate of 93.5%. The possible confounding influence of other factors, namely, gender and age, have also been modeled and found to have much lower predictive power than the presence of the disease. Moreover, smoking habits were found not to have a dominating influence over class discrimination. The main metabolites contributing to this discrimination, as highlighted by multivariate analysis and confirmed by spectral integration, were hippurate and trigonelline (reduced in patients), and β-hydroxyisovalerate, α-hydroxyisobutyrate, N-acetylglutamine, and creatinine (elevated in patients relatively to controls). These results show the valuable potential of NMR-based metabonomics for finding putative biomarkers of lung cancer in urine, collected in a minimally invasive way, which may have important diagnostic impact, provided that these metabolites are found to be specifically disease-related.     

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.     

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.     

Plasma metabonomics as a novel diagnostic approach for major depressive disorder

Major depressive disorder (MDD) is a socially detrimental psychiatric disorder, contributing to increased healthcare expenditures and suicide rates. However, no empirical laboratory-based tests are available to support the diagnosis of MDD. In this study, a NMR-based plasma metabonomic method for the diagnosis of MDD was tested. Proton nuclear magnetic resonance ((1)H NMR) spectra of plasma sampled from first-episode drug-na??ve depressed patients (n = 58) and healthy controls (n = 42) were recorded and analyzed by orthogonal partial least-squares discriminant analysis (OPLS-DA). The OPLS-DA score plots of the spectra demonstrated that the depressed patient group was significantly distinguishable from the healthy control group. Moreover, the method accurately diagnosed blinded samples (n = 26) in an independent replication cohort with a sensitivity and specificity of 92.8% and 83.3%, respectively. Taken together, NMR-based plasma metabonomics may offer an accurate empirical laboratory-based method applicable to the diagnosis of MDD.     

Metabonomic profiles discriminate hepatocellular carcinoma from liver cirrhosis by ultraperformance liquid chromatography-mass spectrometry

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and usually develops in patients with liver cirrhosis (LC). Biomarkers that discriminate HCC from LC are important but are limited. In the present study, an ultraperformance liquid chromatography-mass spectrometry (UPLC-MS)-based metabonomics approach was used to characterize serum profiles from HCC (n = 82), LC (n = 48), and healthy subjects (n = 90), and the accuracy of UPLC-MS profiles and alpha-fetoprotein (AFP) levels were compared for their use in HCC diagnosis. By multivariate data and receiver operating characteristic curves analysis, metabolic profiles were capable of discriminating not only patients from the controls but also HCC from LC with 100% sensitivity and specificity. Thirteen potential biomarkers were identified and suggested that there were significant disturbances of key metabolic pathways, such as organic acids, phospholipids, fatty acids, bile acids, and gut flora metabolism, in HCC patients. Canavaninosuccinate was first identified as a metabolite that exhibited a significant decrease in LC and an increase in HCC. In addition, glycochenodeoxycholic acid was suggested to be an important indicator for HCC diagnosis and disease prognosis. UPLC-MS signatures, alone or in combination with AFP levels, could be an efficient and convenient tool for early diagnosis and screening of HCC in high-risk populations.     

Beneficial metabolic effects of 2',3',5'-tri-acetyl-N6- (3-hydroxylaniline) adenosine in the liver and plasma of hyperlipidemic hamsters

Background

Pharmaceutical research of hyperlipidemia has been commonly pursued using traditional approaches. However, unbiased metabonomics attempts to explore the metabolic signature of hyperlipidemia in a high-throughput manner to understand pathophysiology of the disease process.

Methodology/Principal Findings

As a new way, we performed ¹H NMR-based metabonomics to evaluate the beneficial effects of 2′,3′,5′-tri-acetyl-N₆- (3-hydroxylaniline) adenosine (WS070117) on plasma and liver from hyperlipidemic Syrian golden hamsters. Both plasma and liver profiles provided a clearer distinction between the control and hyperlipidemic hamsters. Compared to control animals, hyperlipidemic hamsters showed a higher content of lipids (triglyceride and cholesterol), lactate and alanine together with a lower content of choline-containing compounds (e.g., phosphocholine, phosphatidylcholine, and glycerophosphocholine) and betaine. As a result, metabonomics-based findings such as the PCA and OPLS-DA plotting of metabolic state and analysis of potential biomarkers in plasma and liver correlated well to the assessment of biochemical assays, Oil Red O staining and in vivo ultrasonographic imaging suggesting that WS070117 was able to regulate lipid content and displayed more beneficial effects on plasma and liver than simvastatin.

Conclusions/Significance

This work demonstrates the promise of applying ¹H NMR metabonomics to evaluate the beneficial effects of WS070117 which may be a good drug candidate for hyperlipidemia.     

Plasma free amino acid profiling of five types of cancer patients and its application for early detection

Background

Recently, rapid advances have been made in metabolomics-based, easy-to-use early cancer detection methods using blood samples. Among metabolites, profiling of plasma free amino acids (PFAAs) is a promising approach because PFAAs link all organ systems and have important roles in metabolism. Furthermore, PFAA profiles are known to be influenced by specific diseases, including cancers. Therefore, the purpose of the present study was to determine the characteristics of the PFAA profiles in cancer patients and the possibility of using this information for early detection.

Methods and Findings

Plasma samples were collected from approximately 200 patients from multiple institutes, each diagnosed with one of the following five types of cancer: lung, gastric, colorectal, breast, or prostate cancer. Patients were compared to gender- and age- matched controls also used in this study. The PFAA levels were measured using high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS). Univariate analysis revealed significant differences in the PFAA profiles between the controls and the patients with any of the five types of cancer listed above, even those with asymptomatic early-stage disease. Furthermore, multivariate analysis clearly discriminated the cancer patients from the controls in terms of the area under the receiver-operator characteristics curve (AUC of ROC >0.75 for each cancer), regardless of cancer stage. Because this study was designed as case-control study, further investigations, including model construction and validation using cohorts with larger sample sizes, are necessary to determine the usefulness of PFAA profiling.

Conclusions

These findings suggest that PFAA profiling has great potential for improving cancer screening and diagnosis and understanding disease pathogenesis. PFAA profiles can also be used to determine various disease diagnoses from a single blood sample, which involves a relatively simple plasma assay and imposes a lower physical burden on subjects when compared to existing screening methods.     

Plasma-free amino acid profiling of cervical cancer and cervical intraepithelial neoplasia patients and its application for early detection

In this study, plasma-free amino acid profiles were used to investigate pre-cancerous cervical intraepithelial neoplasia (CIN) and cervical squamous cell carcinoma (CSCC) metabolic signatures in plasma. Additionally, the diagnostic potential of these profiles was assessed, as well as their ability to provide novel insight into CSCC metabolism and systemic effects. Plasma samples from CIN patients (n = 26), CSCC patients (n = 22), and a control healthy group (n = 35) were analyzed by high-performance liquid chromatography, and their spectral profiles were subjected to the t test for statistical significance. Potential metabolic biomarkers were identified using database comparisons that examine the significance of metabolites. Compared with healthy controls, patients with CIN and CSCC demonstrated lower levels of plasma amino acids; plasma levels of arginine and threonine were increased in CIN patients but were decreased in cervical cancer patients. Additionally, the levels of a larger group of amino acids (aspartate, glutamate, asparagine, serine, glycine, histidine, taurine, tyrosine, valine, methionine, lysine, isoleucine, leucine, and phenylalanine) were gradually reduced from CIN to invasive cancer. These findings suggest that plasma-free amino acid profiling has great potential for improving cancer screening and diagnosis and for understanding disease pathogenesis. Plasma-free amino acid profiles may have the potential be used to determine cancer diagnoses in the early stage from a single blood sample and may enhance our understanding of its mechanisms.     

NMR spectroscopy-based metabolomic study of serum in sulfur mustard exposed patients with lung disease

Sulfur mustard (SM) is a vesication chemical warfare agent for which there is currently no antidote. Despite years of research, there is no common consensus about the pathophysiological basis of chronic pulmonary disease caused by this chemical warfare agent. In this study, we combined chemometric techniques with nuclear magnetic resonance (NMR) spectroscopy to explore the metabolic profile of sera from SM-exposed patients. A total of 29 serum samples obtained from 17 SM-injured patients, and 12 healthy controls were analyzed by Random Forest. Increased concentrations of seven amino acids, glycerol, dimethylamine, ketone bodies, lactate, acetate, citrulline and creatine together with the decreased very low-density lipoproteins (VLDL) levels were observed in patients compared with control subjects. Our study reveals the metabolic profile of sera from SM-injured patients and indicates that NMR-based methods can distinguish these patients from healthy controls.     

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.     

NMR-based metabonomic study of Chinese medicine Gegen Qinlian Decoction as an effective treatment for type 2 diabetes in rats

Metabonomic profiles of the type 2 diabetic rats induced by streptozotocin and high-sugar, high fat diet on the treatment of Gegen Qinlian Decoction (GQD) for 9 weeks were investigated. Rats were randomly divided into five groups: normal control (NC), type 2 diabetes (DM), metformin hydrochloric, GQD in high and low dosages. Plasma samples for ¹H NMR-based metabolomic research, serum samples for clinical biochemistry, and liver and pancreas tissues for histopathology test were collected. Compared with NC rats, metabolic pathways of DM rats were revealed to be altered by pattern analyses of plasma NMR data, which was further correlated with serum biochemistry. Cross-validated scores mean trajectory derived from PLS-DA of NMR spectra demonstrated that GQD significantly restored the abnormal metabolic state in the long run, more potent than metformin hydrochloric. Detailed analysis of the altered metabolite levels indicated that GQD significantly ameliorated the disturbance in glucose metabolism, tricarboxylic acid cycle, lipid metabolism, amino acid metabolism and gut microbial metabolism and N-acetyl group metabolism. The results confirmed the hypoglycemic efficacy of GQD and its ability to ameliorate the diabetic symptoms in a global scale. NMR-based metabonomics approach is helpful for the further understanding of diabetes-related mechanisms.     

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

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

Blood cadmium levels as a marker for early lung cancer detection

BackgroundWe assessed whether blood cadmium levels were associated with incident lung cancer and could be used in the context of a screening program for early-stage lung cancer.Material and methodsWe measured blood cadmium levels among 205 lung cancer patients and 205 matched controls. Cases and controls were matched for sex, age and smoking history (total pack-years, years since cessation for former smokers).ResultsThe odds ratio for those in the highest quartile of cadmium level (versus lowest) was four-fold (OR = 4.41, 95 % CI:2.01–9.67, p < 0.01). The association was present in former smokers (OR = 16.8, 95 % CI:3.96−71.2, p < 0.01), but not in current smokers (OR = 1.23, 95 % CI: 0.34–4.38) or in never smokers (OR not defined). Among former smokers, the association was present in both early- and late-stage lung cancer.ConclusionBlood cadmium levels may be a marker to help with the early detection of lung cancer among former smokers.     

1H NMR-based metabonomics study on serum of renal interstitial fibrosis rats induced by unilateral ureteral obstruction

Renal fibrosis is the common pathway of progressive renal disease with complex pathogenesis. Investigating the metabolic changes in the evaluation process of renal fibrosis may enhance the understanding of its pathogenesis. In this study, (1)H nuclear magnetic resonance ((1)H NMR) measurements combined with multivariate statistical techniques were performed to study the metabolic changes in serum samples of renal interstitial fibrosis (RIF) rats, induced by unilateral ureteral obstruction (UUO). Partial least squares-discriminant analysis (PLS-DA) showed satisfactory clustering between UUO and sham operation (SO) rats, suggesting that the metabolic profiles of the RIF groups are markedly different from those of the controls. Alterations in the levels of some metabolites such as valine, isoleucine, lactate, 3-hydroxybutyrate, alanine, acetate, acetoacetate, pyruvate, and glutamate, with time dependence in UUO rats, were observed in PLS-DA loading plots. These changed metabolites represent potential metabolic biomarkers and provide clues that can elucidate the mechanisms underlying the generation and development of RIF. Enhanced metabolic pathways of lipid and ketone body synthesis were predominant in RIF rats. Energy metabolism seemed to be impaired at the early stage of fibrosis but enhanced at a late stage. Our results suggest that (1)H NMR-based metabonomics can provide novel insights into the pathogenesis of RIF.     

Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning

Pre-eclampsia is a multi-system disorder of pregnancy with major maternal and perinatal implications. Emerging therapeutic strategies are most likely to be maximally effective if commenced weeks or even months prior to the clinical presentation of the disease. Although widespread plasma alterations precede the clinical onset of pre-eclampsia, no single plasma constituent has emerged as a sensitive or specific predictor of risk. Consequently, currently available methods of identifying the condition prior to clinical presentation are of limited clinical use. We have exploited genetic programming, a powerful data mining method, to identify patterns of metabolites that distinguish plasma from patients with pre-eclampsia from that taken from healthy, matched controls. High-resolution gas chromatography time-of-flight mass spectrometry (GC-tof-MS) was performed on 87 plasma samples from women with pre-eclampsia and 87 matched controls. Normalised peak intensity data were fed into the Genetic Programming (GP) system which was set up to produce a model that gave an output of 1 for patients and 0 for controls. The model was trained on 50% of the data generated and tested on a separate hold-out set of 50%. The model generated by GP from the GC-tof-MS data identified a metabolomic pattern that could be used to produce two simple rules that together discriminate pre-eclampsia from normal pregnant controls using just 3 of the metabolite peak variables, with a sensitivity of 100% and a specificity of 98%. Thus, pre-eclampsia can be diagnosed at the level of small-molecule metabolism in blood plasma. These findings justify a prospective assessment of metabolomic technology as a screening tool for pre-eclampsia, while identification of the metabolites involved may lead to an improved understanding of the aetiological basis of pre-eclampsia and thus the development of targeted therapies.     

1H NMR-Based Metabolite Profiling of Plasma in a Rat Model of Chronic Kidney Disease

Chronic kidney disease (CKD) is characterized by the gradual loss of the kidney function to excrete wastes and fluids from the blood. ¹H NMR-based metabolomics was exploited to investigate the altered metabolic pattern in rats with CKD induced by surgical reduction of the renal mass (i.e., 5/6 nephrectomy (5/6 Nx)), particularly for identifying specific metabolic biomarkers associated with early of CKD. Plasma metabolite profiling was performed in CKD rats (at 4- or 8-weeks after 5/6 Nx) compared to sham-operated rats. Principle components analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) score plots showed a significant separation between the groups. The resulting metabolic profiles demonstrated significantly increased plasma levels of organic anions, including citrate, β-hydroxybutyrate, lactate, acetate, acetoacetate, and formate in CKD. Moreover, levels of alanine, glutamine, and glutamate were significantly higher. These changes were likely to be associated with complicated metabolic acidosis in CKD for counteracting systemic metabolic acidosis or increased protein catabolism from muscle. In contrast, levels of VLDL/LDL (CH₂)_n and N-acetylglycoproteins were decreased. Taken together, the observed changes of plasma metabolite profiles in CKD rats provide insights into the disturbed metabolism in early phase of CKD, in particular for the altered metabolism of acid-base and/or amino acids.     

Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits

Impaired glucose tolerance (IGT) which precedes overt type 2 diabetes (T2DM) for decades is associated with multiple metabolic alterations in insulin sensitive tissues. In an UPLC-qTOF-mass spectrometry-driven non-targeted metabonomics approach we investigated plasma as well as spot urine of 51 non-diabetic, overnight fasted individuals aiming to separate subjects with IGT from controls thereby identify pathways affected by the pre-diabetic metabolic state. We could clearly demonstrate that normal glucose tolerant (NGT) and IGT subjects clustered in two distinct groups independent of the investigated metabonome. These findings reflect considerable differences in individual metabolite fingerprints, both in plasma and urine. Pre-diabetes associated alterations in fatty acid-, tryptophan-, uric acid-, bile acid-, and lysophosphatidylcholine-metabolism, as well as the TCA cycle were identified. Of note, individuals with IGT also showed decreased levels of gut flora-associated metabolites namely hippuric acid, methylxanthine, methyluric acid, and 3-hydroxyhippuric acid. The findings of our non-targeted UPLC-qTOF-MS metabonomics analysis in plasma and spot urine of individuals with IGT vs NGT offers novel insights into the metabolic alterations occurring in the long, asymptomatic period preceding the manifestation of T2DM thereby giving prospects for new intervention targets.