UPLC-TOF MS-Based Metabonomic Study on Coadministration of Huperzine A and Ligustrazine Phosphate for Treatment of Alzheimer’s Disease

This work was designed to assess the holistic efficacy of concurrent treatment with huperzine A (HA) and ligustrazine phosphate (LP) in the animal model, and to interpret the metabonomics characteristics of the poly-therapy in treating Alzheimer’s disease (AD). Metabolic profiling of the brain was performed using ultra performance liquid chromatography coupled to time-of-flight mass spectroscopy (UPLC-TOF MS). Principal component analysis and partial least squares discriminant analysis were utilized to classify and reveal the differences among the control, model and treatment groups. Ten potential biomarkers have been finally gained. The metabonomic results revealed that the coadministration of HA and LP might slow down the progression of AD through synergistic therapeutic efficacies, exhibiting better effect on amnesia compared with the mono-drug therapy. Importantly, the UPLC-TOF MS-based metabonomic approach would be a useful technique for the diagnosis of AD, the discovery of metabolic biomarkers and the evaluation of the treatment effects.

     

UPLC-TOF MS-Based Metabonomic Study on Coadministration of Huperzine A and Ligustrazine Phosphate for Treatment of Alzheimer��s Disease

This work was designed to assess the holistic efficacy of concurrent treatment with huperzine A (HA) and ligustrazine phosphate (LP) in the animal model, and to interpret the metabonomics characteristics of the poly-therapy in treating Alzheimer??s disease (AD). Metabolic profiling of the brain was performed using ultra performance liquid chromatography coupled to time-of-flight mass spectroscopy (UPLC-TOF MS). Principal component analysis and partial least squares discriminant analysis were utilized to classify and reveal the differences among the control, model and treatment groups. Ten potential biomarkers have been finally gained. The metabonomic results revealed that the coadministration of HA and LP might slow down the progression of AD through synergistic therapeutic efficacies, exhibiting better effect on amnesia compared with the mono-drug therapy. Importantly, the UPLC-TOF MS-based metabonomic approach would be a useful technique for the diagnosis of AD, the discovery of metabolic biomarkers and the evaluation of the treatment effects.     

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GC × GC-TOFMS) coupled with pattern recognition methods was applied to analyze plasma from diabetic patients and healthy controls. After sample preparation and GC × GC-TOFMS analysis, collected data were transformed, the peak alignment between different chromatograms was performed to generate the metabolites’ peak table, then orthogonal signal correction filtered partial least-squares discriminant analysis (OSC-PLSDA) was carried out to model the data and discover metabolites with a significant concentration change in diabetic patients. With the method above, diabetic patients and healthy controls could be correctly distinguished based on the metabolic abnormity in plasma. Five potential biomarkers including glucose, 2-hydroxyisobutyric acid, linoleic acid, palmitic acid and phosphate were identified. It was found that elevated free fatty acids were essential pathophysiological factors in diabetes mellitus which reflected either the hyperglycemia or the deregulation of fatty acids metabolism. These potential biomarkers in plasma, e.g. palmitic acid, linoleic acid and 2-hydroxybutyric acid might be helpful in the diagnosis or further study of diabetes mellitus. This study shows the practicability and advantage of GC × GC-TOFMS coupled with data analysis and mining for metabonomics in biomarker discovery.     

Urinary signature of anabolic steroids and glucocorticoids in humans by LC-MS

A metabonomic strategy based on LC-MS was employed to investigate the metabolic profile of urine samples from 20 athletes who had been tested positive for corticoids and anabolic steroids and 29 controls. In this aim, different sample preparations and chromatographic conditions were compared. The acquired LC-MS data of doped athletes and controls were subjected to analysis of variance (ANOVA) and principal component analysis (PCA). Using this approach, molecular signature of human urine was obtained showing that metabonomics could be a complementary tool to discriminate different urinary profiles and to track down metabolic changes in humans.     

Metabonomic classification and detection of small molecule biomarkers of malignant pleural effusions

To date, most research has been focused on the benign molecules in pleural effusions, and diagnosis of malignant ones still remains challenging. In the present study, targeting the small molecules as potential biomarkers to predict the malignancy of the effusions, the metabolic profiles of 81 clinical pleural effusions (41 malignant effusions from lung cancer and 40 benign ones) were investigated through a NMR-based metabonomic approach. In ¹H NMR analysis, a total of ten small molecules in the effusions were simultaneously determined. Significantly higher mean values of valine, lactate, and alanine and markedly lower signal intensities of acetoacetate, trimethylamine-N-oxide, and α- and β-glucose were observed in malignant pleural effusions compared with those in benign ones. DFA modeling of NMR spectra subjected to a validation allowed the malignant effusions to be discriminated from benign ones in both training and validation groups. Currently, the conventional clinical analyses on chemical constituents in effusions could not provide a reliable prediction of malignancy of the effusions; the present results revealed that the small molecules might serve as useful biomarkers for diagnosis of the effusions, and the present NMR-based metabonomic approach provided a valuable potential to rapidly and sensitively predict the malignancy of the pleural effusions.

Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy

Type 2 diabetes mellitus (T2DM) and its attendant complications, such as diabetic nephropathy (DN), impose a significant societal and economic burden. The investigation of discovering potential biomarkers for T2DM and DN will facilitate the prediction and prevention of diabetes. Phospholipids (PLs) and their metabolisms are closely allied to nosogenesis and aggravation of T2DM and DN. The aim of this study is to characterize the human plasma phospholipids in T2DM and DN to identify potential biomarkers of T2DM and DN. Normal phase liquid chromatography coupled with time of flight mass spectrometry (NPLC-TOF/MS) was applied to the plasma phospholipids metabolic profiling of T2DM and DN. The plasma samples from control (n = 30), T2DM subjects (n = 30), and DN subjects (n = 52) were collected and analyzed. The significant difference in metabolic profiling was observed between healthy control group and DM group as well as between control group and DN group by the help of partial least squares discriminant analysis (PLS-DA). PLS-DA and one-way analysis of variance (ANOVA) were successfully used to screen out potential biomarkers from complex mass spectrometry data. The identification of molecular components of potential biomarkers was performed on Ion trap-MS/MS. An external standard method was applied to quantitative analysis of potential biomarkers. As a result, 18 compounds in 7 PL classes with significant regulation in patients compared with healthy controls were regarded as potential biomarkers for T2DM or DN. Among them, 3 DM-specific biomarkers, 8 DN-specific biomarkers and 7 common biomarkers to DM and DN were identified. Ultimately, 2 novel biomarkers, i.e., PI C18:0/22:6 and SM dC18:0/20:2, can be used to discriminate healthy individuals, T2DM cases and DN cases from each other group.     

A metabonomic analysis of urine from rats treated with rhizoma alismatis using ultra‐performance liquid chromatography/mass spectrometry

A metabonomic approach based on ultra‐performance liquid chromatography coupled to mass spectrometry (UPLC/MS) was used to study the nephrotoxicity of rhizoma alismatis (RA) in rats. Potential biomarkers of RA toxicity were identified and the toxicological mechanism is discussed. Urine samples were collected from control and treated rats at various stages and analyzed by UPLC/MS in positive ionization mode. Histopathological analysis was used to evaluate renal function. The differences in the metabolic profiles of the control and treated rats were clearly distinguishable with principal components analysis (PCA) of the chromatographic data, and significant changes in 13 metabolite biomarkers were detected in the urine. This metabonomic method combined with PCA could discriminate the treated rats from the control rats on days 60, 120, and 180 after treatment, before serious organic renal damage was apparent on day 180 with histopathology. This research indicates that UPLC/MS‐based metabonomic analysis of urine samples can be used to predict the chronic nephrotoxicity induced by rhizoma alismatis. Copyright © 2011 John Wiley & Sons, Ltd.     

Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method

Late diagnosis of hepatocarcinoma (HCC) is one of the most primary factors for the poor survival of patients. Thereby, identification of sensitive and specific biomarkers for HCC early diagnosis is of great importance in biological medicine to date. In the present study, serum metabolites of the HCC patients and healthy controls were investigated using the improved liquid chromatography–mass spectrometry (LC/MS). A wavelet-based method was utilized to find and align peaks of LC–MS. The characteristic peaks were selected by performing a two-sample t test statistics (p value <0.05). Clustering analysis based on principal component analysis showed a clear separation between HCC patients and healthy individuals. The serum metabolite, namely 1-methyladenosine, was identified as the characteristic metabolite for HCC. Moreover, receiver–operator curves were calculated with 1-methyladenosine and/or alpha fetal protein (AFP). The higher area under curve value was achieved in 1-methyladenosine group than AFP group (0.802 vs. 0.592), and the diagnostic model combining 1-methyladenosine with AFP exhibited significant improved sensitivity, which could identify those patients who missed the diagnosis of HCC by determining serum AFP alone. Overall, these results suggested that LC/MS-based metabonomic study is a potent and promising strategy for identifying novel biomarkers of HCC.     

Human Plasma Metabolic Profiles of Coronary Heart Disease by Gas Chromatography-Mass Spectrometry with Monte Carlo Tree Approach

Metabolomics is a useful approach to explore systemic metabolic variation and to elucidate disease mechanisms. In this study, human plasma metabolic profiles of coronary heart disease (CHD) patients and healthy controls were obtained by gas chromatography-mass spectrometry (GC-MS). A relatively new pattern recognition method, the Monte Carlo tree (MCTree) approach, was used to explore metabolic differences between CHD patients and healthy controls. In this way, CHD patients with different severity of coronary atherosclerosis were classified by the corresponding metabolic profiles. Furthermore, important metabolites contributing to the classification were screened and identified by their mass spectra. Several potential biomarkers were discussed in some detail. The results demonstrated that the proposed method might be a useful tool for discovering metabolic abnormalities and potential biomarkers for diseases.     

Metabolomic profiling of human urine in hepatocellular carcinoma patients using gas chromatography/mass spectrometry

With the technique of metabolomics, gas chromatography/mass spectrometry (GC/MS), urine or serum metabolites can be assayed to explore disease biomarkers. In this work, we present a metabolomic method to investigate the urinary metabolic difference between hepatocellular carcinoma (HCC, n = 20) male patients and normal male subjects (n = 20). The urinary endogenous metabolome was assayed using chemical derivatization followed by GC/MS. After GC/MS analysis, 103 metabolites were detected, of which 66 were annotated as known compounds. By a two sample t-test statistics with p < 0.05, 18 metabolites were shown to be significantly different between the HCC and control groups. A diagnostic model was constructed with a combination of 18 marker metabolites or together with alphafetoprotein, using principal component analysis and receiver-operator characteristic curves. The multivariate statistics of the diagnostic model yielded a separation between the two groups with an area under the curve value of 0.9275. This non-invasive technique of identifying HCC biomarkers from urine may have clinical utility.     

Comprehensive metabonomic analysis of heart tissue from isoproterenol‐induced myocardial infarction rat based on reversed‐phase and hydrophilic interaction chromatography coupled to mass spectrometry

We aim to describe the metabonomic characteristics of myocardial infarction rats. High‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry was utilized to develop a metabonomic method of the heart homogenates of myocardial infarction rats. Hydrophilic interaction chromatography allows the analysis of high polar metabolites, providing complementary information to reversed‐phase liquid chromatography. We combined reversed phase and hydrophilic interaction chromatographic separations to analyze 18 samples, ten from myocardial infarction rat hearts and eight from normal rat hearts. A total of 16 potential biomarkers in rat heart tissue were screened out, primarily related to oxidative stress, nitric oxide damage, taurine, and hypotaurine metabolism and sphingolipid metabolism. This research showed that a comprehensive metabonomic study is a useful tool to reveal the underlying mechanism of myocardial infarction.     

Normalization using a tagged-internal standard assay for analysis of antibody arrays and the evaluation of serological biomarkers for liver disease

For minimizing systemic experimental variation in the analysis of antibody array data, we developed a novel median-centered/IgM-tagged-internal standard (TIS) assay normalization using median-centering and TIS assay-based determination of serum IgM concentrations. We evaluated five normalization methods by analyzing correlation coefficients and coefficients of variation for six serum proteins using human serum samples from normal controls (n = 25) and patients with liver cirrhosis (n = 25) or hepatocellular carcinoma (HCC; n = 29). Median-centered normalization improved correlation coefficients, while IgM-based normalizations improved coefficients of variation. The TIS assay was more efficient, economical, and reproducible for determining IgM concentrations than enzyme-linked immunosorbent assay. Additionally, we normalized antibody array data for six serum proteins using the median-centered/IgM-TIS assay, and evaluated serum biomarkers through distribution analysis of normalized fluorescence intensities and receiver operating characteristic analyses for the diagnosis of liver cirrhosis and HCC. Apolipoprotein A-1 and a combination of alpha-fetoprotein and C-reactive protein were determined to be potential serological biomarkers for liver cirrhosis and HCC, respectively. Thus, median-centered/IgM-TIS assay normalization is a useful approach for analyzing antibody array data and evaluating serological biomarkers for the diagnosis of liver disease or cancers.     

基于花生四烯酸代谢通路研究水飞蓟素对脂多糖诱导炎症模型影响

以脂多糖类似物(KLA)诱导的RAW264.7细胞为研究对象,采用代谢组学研究手段,研究水飞蓟素对脂多糖诱导炎症模型中花生四烯酸代谢通路的影响。以超高效液相色谱-三重四极杆质谱联用为平台,对不同浓度水飞蓟素作用下KLA诱导RAW264.7炎症细胞分泌的类二十烷酸代谢物进行定量分析,通过考察主成分分析(PCA)、正交偏最小二乘法判别分析(OPLS-DA)的VIP值和Kruskal-Wallis秩和检验结果显著性差异(P)值筛选代谢标记物。建立了59种类二十烷酸(含15种同位素内标)在5 min内实现快速分离的液相色谱-质谱联用方法;确定了细胞存活率在58%~80%的水飞蓟素浓度为50~150μmol/L;筛选出数据处理结果同时满足变异权重参数(VIP)值1且结果P值0.05的类二十烷酸代谢标记物12-OxoLeukotriene B4(12-OxoLTB4);通过分析柱状图和炎症信号通路,确定水飞蓟素借助其抗氧自由基特性发挥抗炎作用,通过抑制脂氧合酶-5(5-LOX)的活性及阻断5-LOX代谢通路中产生氧自由基的脂质过氧化反应来减少氧自由基及过氧化物的形成。综上所述,所建立的方法能快速准确地定量分析多种类二十烷酸,并从代谢组学角度解释了水飞蓟素的抗炎机制。     

Headspace-mass spectrometry determination of benzene, toluene and the mixture of ethylbenzene and xylene isomers in soil samples using chemometrics

A simple and fast method has been developed for the determination of benzene, toluene and the mixture of ethylbenzene and xylene isomers (BTEX) in soils. Samples were introduced in 10 mL standard glass vials of a headspace (HS) autosampler together with 150 μL of 2,6,10,14-tetramethylpentadecane, heated at 90 °C for 10 min and introduced in the mass spectrometer by using a transfer line heated at 250 °C as interface. The volatile fraction of samples was directly introduced into the source of the mass spectrometer which was scanned from m/z 75 to 110. A partial least squares (PLS) multivariate calibration approach based on a classical 3³ calibration model was build with mixtures of benzene, toluene and o-xylene in 2,6,10,14-tetramethylpentadecane for BTEX determination. Results obtained for BTEX analysis by HS-MS in different types of soil samples were comparables to those obtained by the reference HS-GC-MS procedure. So, the developed procedure allowed a fast identification and prediction of BTEX present in the samples without a prior chromatographic separation.     

Identifying potential serum biomarkers of breast cancer through targeted free fatty acid profiles screening based on a GC–MS platform

Recent advances suggest that abnormal fatty acid metabolism highly correlates with breast cancer, which provide clues to discover potential biomarkers of breast cancer. This study aims to identify serum free fatty acid (FFA) metabolic profiles and screen potential biomarkers for breast cancer diagnosis. Gas chromatography–mass spectrometry and our in-house fatty acid methyl ester standard substances library were combined to accurately identify FFA profiles in serum samples of breast cancer patients and breast adenosis patients (as controls). Potential biomarkers were screened by applying statistical analysis. A total of 18 FFAs were accurately identified in serum sample. Two groups of patients were correctly discriminated by the orthogonal partial least squares–discriminant analysis model based on FFA profiles. Seven FFA levels were significantly higher in serum from breast cancer patients than that in controls, and exhibited positive correlation with malignant degrees of disease. Furthermore, five candidates (palmitic acid, oleic acid, cis-8,11,14-eicosatrienoic acid, docosanoic acid and the ratio of oleic acid to stearic acid) were selected as potential serum biomarkers for differential diagnosis of breast cancer. Our study will help to reveal the metabolic signature of FFAs in breast cancer patients, and provides valuable information for facilitating clinical noninvasive diagnosis.     

Metabonomics study of atherosclerosis rats by ultra fast liquid chromatography coupled with ion trap-time of flight mass spectrometry

An ultra fast liquid chromatography coupled with IT-TOF mass spectrometry (UFLC/MS-IT-TOF) metabonomic approach was employed to study the plasma and urine metabolic profiling of atherosclerosis rats. Acquired data were subjected to principal component analysis (PCA) for differentiating the atherosclerosis and the control groups. Potential biomarkers were screened by using S-plot and were identified by the accurate mass and MSⁿ fragments information obtained from UFLC/MS-IT-TOF analysis. 12 metabolites in rat plasma and 8 metabolites in urine were identified as potential biomarkers. Concentrations of leucine, phenylalanine, tryptophan, acetylcarnitine, butyrylcarnitine, propionylcarnitine and spermine in plasma and 3-O-methyl-dopa, ethyl N2-acetyl-l-argininate, leucylproline, glucuronate, t6A N(6)-(N-threonylcarbonyl)-adenosine and methyl-hippuric acid in urine decreased in atherosclerosis rats. Ursodeoxycholic acid, chenodeoxycholic acid, LPC (C16:0), LPC (C18:0) and LPC (C18:1) in plasma and hippuric acid in urine were in higher levels in atherosclerosis rats. The alterated metabolites demonstrated abnormal metabolism of phenylalanine, tryptophan, bile acids and amino acids. This research proved that metabonomics is a promising tool for disease research.     

代谢组学应用于糖肾方治疗糖尿病肾病的疗效评价

建立了基于超高效液相色谱-飞行时间质谱(UPLC/TOF MS)分析技术的血浆代谢指纹谱,应用多元统计分析方法评价糖尿病肾病患者血浆代谢物变化差异及糖肾方的干预效果。通过研究糖肾方干预糖尿病肾病血浆内源性代谢物的变化,探索与该疾病密切相关的代谢途径,评价糖肾方的治疗效果。结果发现: 经糖肾方治疗后,血浆内源性代谢物发生了明显变化,磷脂代谢、脂肪酸代谢、氨基酸代谢、嘌呤嘧啶代谢、固醇类代谢等多个代谢途径得到纠正。本研究基于UPLC/TOF MS的代谢组学方法,能够从整体水平反映疾病治疗过程中代谢网络的变化趋势,证实糖肾方具有治疗糖尿病肾病的临床疗效并有助于阐释药物作用机理。     

Metabolic characterization of the early stage of hepatic fibrosis in rat using GC‐TOF/MS and multivariate data analyses

The aim of this study was to explore the changes in the urine metabolic spectrum in rats with the early stage of liver fibrosis using gas chromatography–time of flight/mass spectrometry (GC‐TOF/MS), try to search for potential biomarkers and elucidate the probably metabonomic pathogenesis. The early stage of liver fibrosis was established with a single subcutaneous injection of carbon tetrachloride twice each week for 4 weeks continuously. At the end of the experiment, GC‐TOF/MS technology with multivariate statistical approaches such as principal component analysis, partial least squares‐discriminant analysis and orthogonal partial least squares‐discriminant analysis was used to analyze the changes in the metabolic spectrum trajectory and identify potential biomarkers. Twelve potential biomarkers in the model group, such as succinic acid, threonine and lactose, were selected, which indicate that the metabonomic pathogenesis of the early stage of liver fibrosis may be related to disorders of energy metabolism, amino acid metabolism and fatty acid metabolism.     

Serum Metabolic Fingerprinting to Detect Human Nasopharyngeal Carcinoma Based on Gas Chromatography-Mass Spectrometry and Partial Least Squares-Linear Discriminant Analysis

In this paper, metabolic fingerprints were obtained from 102 nasopharyngeal carcinoma (NPC) patients and 107 healthy adults by gas chromatography-mass spectrometry (GC/MS). Partial least squares-discriminant analysis (PLS-DA) has revealed a pattern recognition discriminating the patients from controls, which sensitivity is 89.72% (96/107) and specificity is 85.29% (87/102). Furthermore, double-blind experiment was carried out and satisfactory results were obtained (total correct rate 87.30%). In addition, metabolites that most strongly influence this separation were obtained. The results indicated that a metabonomic approach is feasible and efficient and deserves further evaluation as a potential novel strategy for the detection of nasopharyngeal carcinoma.     

Reconstruction and analysis of correlation networks based on GC–MS metabolomics data for young hypertensive men

The awareness, treatment, and control rates of hypertension for young adults are much lower than average. It is urgently needed to explore the variances of metabolic profiles for early diagnosis and treatment of hypertension. In current study, we applied a GC–MS based metabolomics platform coupled with a network approach to analyze plasma samples from young hypertensive men and age-matched healthy controls. Our findings confirmed distinct metabolic footprints of young hypertensive men. The significantly altered metabolites between two groups were enriched for the biological module of amino acids biosynthesis. The correlations of GC–MS metabolomics data were then visualized as networks based on Pearson correlation coefficient (threshold = 0.6). The plasma metabolites identified by GC–MS and the significantly altered metabolites (P < 0.05) between patients and controls were respectively included as nodes of a network. Statistical and topological characteristics of the networks were studied in detail. A few amino acids, glycine, lysine, and cystine, were screened as hub metabolites with higher values of degree (k), and also obtained highest scores of three centrality indices. The short average path lengths and high clustering coefficients of the networks revealed a small-world property, indicating that variances of these amino acids have a major impact on the metabolic change in young hypertensive men. These results suggested that disorders of amino acid metabolism might play an important role in predisposing young men to developing hypertension. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism underlying complex diseases.