Classification and differential metabolite discovery of liver diseases based on plasma metabolic profiling and support vector machines

Discovery of differential metabolites is the focus of metabonomics study. It has very important applications in pathogenesis and disease classification. The aim of this work is to identify differential metabolites for classifying the patients with hepatocellular carcinoma, cirrhosis and hepatitis based on metabolic profiling data analyzed by gas chromatography-time of flight mass spectrometry. A two-stage feature selection algorithm, F-SVM, combining F-score in analysis of variance and support vector machine (SVM), was applied in discovering discriminative metabolites for three different types of liver diseases. The results show that the accuracy rate of the double cross-validation was 73.68±2.98%. 22 important differential metabolites selected by F-SVM were identified and related pathophysiological process of liver diseases was set forth. We conclude that F-SVM is quite feasible to be applied in the selection of biologically relevant features in metabonomics.     

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.     

Metabonomics investigation of human urine after ingestion of green tea with gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry and (1)H NMR spectroscopy

A method using gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS) and (1)H NMR with pattern recognition tools such as principle components analysis (PCA) was used to study the human urinary metabolic profiles after the intake of green tea. From the normalized peak areas obtained from GC/MS and LC/MS and peak heights from (1)H NMR, statistical analyses were used in the identification of potential biomarkers. Metabolic profiling by GC/MS provided a different set of quantitative signatures of metabolites that can be used to characterize the molecular changes in human urine samples. A comparison of normalized metabonomics data for selected metabolites in human urine samples in the presence of potential overlapping peaks after tea ingestion from LC/MS and (1)H NMR showed the reliability of the current approach and method of normalization. The close agreements of LC/MS with (1)H NMR data showed that the effects of ion suppression in LC/MS for early eluting metabolites were not significant. Concurrently, the specificity of detecting the stated metabolites by (1)H NMR and LC/MS was demonstrated. Our data showed that a number of metabolites involved in glucose metabolism, citric acid cycle and amino acid metabolism were affected immediately after the intake of green tea. The proposed approach provided a more comprehensive picture of the metabolic changes after intake of green tea in human urine. The multiple analytical approach together with pattern recognition tools is a useful platform to study metabolic profiles after ingestion of botanicals and medicinal plants.     

膀胱癌血清及尿液代谢组学研究

应用代谢组学研究方法,对与膀胱癌(Bladder cancer,BC)发病相关的生物标志物进行筛选,采用液相色谱-电喷雾质谱(LC-ESI/MS)联用技术对20名膀胱癌患者与24名正常人的血清和尿液进行研究.多变量统计分析结果表明,膀胱癌患者和正常人聚类明显,血清和尿液中分别发现13个潜在标志物.其中,(2E,6E,8E)-二十二碳三烯-1-醇、7-((1S,2S)-2-(庚胺)环己基)庚酸和(11E,14E,17E)-三烯-二十碳-1-醇首次在血清中发现,有潜力成为膀胱癌诊断标志物.液相色谱-质谱联用结合多变量分析的代谢组学研究技术在膀胱癌诊断中展现出巨大潜力.     

Fecal metabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultra performance liquid chromatography-mass spectrometry

Fecal metabolome of healthy humans and patients suffering from liver cirrhosis and hepatocellular carcinoma (HCC) were studied using ultra performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF MS). Metabolic features detected by the method were then statistically treated using partial least squares to latent structure-discriminant analysis (PLS-DA) models to discriminate between healthy and diseased states. PLS-DA was also used to discriminate between cirrhosis and HCC stressed fecal metabolomes and to identify potential biomarkers for cirrhosis and HCC that are expressed at significantly different amounts in fecal metabolomes. Score plots of pattern recognition analysis distinguished liver cirrhosis and HCC patients from healthy humans. Based on the variable of importance in the project (VIP) values and S-plots, six metabolites were considered as potential biomarkers with a strong increase in lysophosphatidylcholines and a dramatic decrease in bile acids and bile pigments in patients with liver cirrhosis and HCC in comparison with healthy humans. Results demonstrate the potential of UPLC-MS as an efficient and convenient method that can be applied to screen fecal samples and aid in the early diagnosis of cirrhosis and hepatocellular carcinoma.     

高血压病肝阳上亢证血清代谢指纹图谱研究

根据临床高血压病肝阳上亢证的诊断标准,选择典型病例,以高效液相色谱-质谱联用技术分析高血压病肝阳上亢证患者和健康志愿者的血清成分,采用"中药色谱指纹图谱相似度评价系统"对齐、匹配色谱峰,采用PCA方法进行模式识别,以MS检测标记物分子量。两组高效液相色谱数据可以在得分图实现分类,与健康志愿者作比较,患者机体相关代谢发生显著变化。高效液相色谱质谱联用结合PCA模式识别的代谢组学方法可用于研究复杂条件下机体病理的生理变化,为高血压病肝阳上亢证病证诊断提供科学依据。     

基于液相色谱-质谱联用技术的代谢组学方法在细胞种属分类中的应用

细胞内的代谢产物可以反映细胞的生理状态。为了考察基于胞内代谢物的指纹图谱对不同种属细胞进行区分的可行性,利用基于超高效液相色谱-高分辨飞行时间质谱(UPLC-TOF MS)技术的代谢组学方法对5种不同来源的细胞进行分类,获得了小分子代谢产物的差异表达谱,并采用主成分分析(PCA)数据处理方法对各类细胞进行模式识别。研究结果表明,不同的细胞种属之间均能呈现显著性差异。该研究可从分子水平对细胞种属进行分类,为细胞种属的鉴定与评价提供了一种新的技术方法,为细胞组学的深入研究提供了一种潜在的、非常具有应用前景的技术手段。     

Identification of the major metabolites of tectorigenin in rat bile by liquid chromatography combined with time-of-flight and ion trap tandem mass spectrometry

A novel methodology for the identification of tetorigenin and its metabolites in rat bile has been created using liquid chromatography (LC) combined with time-of-flight (TOF) and ion trap multiple mass spectrometry (IT-MSn). As a means to discriminate amongst unknown organic compounds in complex biological matrices, the proposed methodology relies upon the combination of LC/TOF-MS to provide accurate mass measurements in generating a molecular formula while benefiting from the complementary structural information provided by the LC/IT-MSn. In this study, the combined approach has been applied to the metabolic fingerprinting chromatograms of rat bile samples before and after tectorigenin administration. All possible metabolites are investigated based on accurate mass data and isotope function using LC/TOF-MS and structural confirmation using LC/IT-MSn. Seven phase II metabolities of tectorigenin in rat bile have been successfully elucidated using this novel LC approach and are being reported for the first time.     

Metabolite profiling analysis of hepatitis B virus–induced liver cirrhosis patients with minimal hepatic encephalopathy using gas chromatography-time-of-flight mass spectrometry and ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry

This study used gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) and ultra-performance liquid chromatography-quadrupole TOFMS (UPLC-QTOFMS) metabonomic analytical techniques in combination with bioinformatics and pattern recognition analysis methods to analyze the serum metabolite profiling of hepatitis B virus (HBV)–induced liver cirrhosis patients with minimal hepatic encephalopathy (MHE), to find the specific biomarkers of MHE, to reveal the pathogenesis of MHE, and to determine a promising approach for early diagnosis of MHE. Serum samples of 100 normal controls (NC group), 29 HBV-induced liver cirrhosis patients with MHE (MHE group), and 24 HBV-induced liver cirrhosis patients without MHE [comprising 12 cases of compensated cirrhosis (CS group) and 12 cases of decompensated cirrhosis (DS group)] were collected and employed into GC-TOFMS and UPLC-QTOFMS platforms for serum metabolite detection; the outcome data were then analyzed using principal component analysis and orthogonal partial least squares-discriminant analysis (OPLS-DA). There were no significant differential metabolites between the NC group and the CS group. A series of key differential metabolites were detected. According to the variable influence in projection values and P-values, 60 small-molecule metabolites were considered to be dysregulated in the MHE group (compared to the NC group); 27 of these 60 dysregulated differential metabolites were considered to be the potential biomarkers (see Table 4, marked in bold); 66 small-molecule metabolites were considered to be dysregulated in the DS group (compared to the NC group); 34 of these 66 dysregulated differential metabolites were considered to be the potential biomarkers (see Table 5, marked in bold). According to the fold-change values, 9 of these 27 metabolites, namely valine, oxalic acid, erythro-sphingosine, 4,7,10,13,16,19-docosahexaenoic acid, isoleucine, allo-isoleucine, thyroxine, rac-octanoyl carnitine, and tocopherol (vitamin E), were downregulated in the MHE group (compared to the NC group); the other 18, namely adenine, glycochenodeoxycholic acid, fucose, allothreonine, glycohyocholic acid, glycoursodeoxycholic acid, tyrosine, taurocheno-deoxycholate, phenylalanine, 2-hydroxy-3-methyl-butanoic acid, hydroxyacetic acid, taurocholate, sorbitol, rhamnose, tauroursodeoxycholate, tolbutamide, pyroglutamic acid, and malic acid, were upregulated; 6 of these 34 metabolites were downregulated in the DS group (compared to the NC group), and the other 28 were upregulated, as shown in Table 5. (a) GC-TOFMS and UPLC-QTOFMS metabonomic analytical platforms can detect a range of metabolites in the serum; this might be of great help to study the pathogenesis of MHE and may provide a new approach for the early diagnosis of MHE. (b) Metabonomics analysis in combination with pattern recognition analysis might have great potential to distinguish the HBV-induced liver cirrhosis patients who have MHE from the normal healthy population and HBV-induced liver cirrhosis patients without MHE.     

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.     

Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.     

Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations

In this study, urinary metabolites from liver cancer patients and healthy volunteers were studied by a metabonomic method based on ultra performance liquid chromatography coupled to mass spectrometry. Both hydrophilic interaction chromatography (HILIC) and reversed-phase liquid chromatography (RPLC) were used to separate the urinary metabolites. Principle component analysis (PCA) and partial least squares to latent structure-discriminant analysis (PLS-DA) models were built to separate the healthy volunteers from the liver cancer patients and to find compounds that are expressed in significantly different amounts between the two populations. 21 metabolite ions were considered as potential biomarkers according to the Variable importance in the Project (VIP) value and S-plot. Compared with RPLC, a more sensitive and stable response can be recorded in HILIC mode due to the high content of organic solvent used. Moreover, the liver cancer group and the healthy volunteers can be better separated based on the data from the HILIC separation, which indicates that HILIC is suitable for urinary metabonomic analysis. In HILIC mode, several polar compounds related to arginine and proline metabolism, alanine and aspartate metabolism, lysine degradation, nicotinate and nicotinamide metabolism were found to be significantly changed in the concentrations of the two different populations: healthy and cancer. In contrast, in RPLC mode, these changed compounds are related to fatty acids oxidation.     

Improving signal-to-noise ratios of liquid chromatography-tandem mass spectrometry peaks using noise frequency spectrum modification between two consecutive matched-filtering procedures

This paper reports a simple chemometric technique to alter the noise spectrum of liquid chromatography-tandem mass spectrometry (LC-MS-MS) chromatogram between two consecutive matched filter procedures to improve the peak signal-to-noise (S/N) ratio enhancement. This technique is to multiply one match-filtered LC-MS-MS chromatogram with another artificial chromatogram added with thermal noises prior to the second matched filter. Because matched filter cannot eliminate low-frequency components inherent in the flicker noises of spike-like sharp peaks randomly riding on LC-MS-MS chromatograms, efficient peak S/N ratio improvement cannot be accomplished using one-step or consecutive matched filter procedures to process LC-MS-MS chromatograms. In contrast, when the match-filtered LC-MS-MS chromatogram is conditioned with the multiplication alteration prior to the second matched filter, much better efficient ratio improvement is achieved. The noise frequency spectrum of match-filtered chromatogram, which originally contains only low-frequency components, is altered to span a boarder range with multiplication operation. When the frequency range of this modified noise spectrum shifts toward higher frequency regime, the second matched filter, working as a low-pass filter, is able to provide better filtering efficiency to obtain higher peak S/N ratios. Real LC-MS-MS chromatograms containing random spike-like peaks, of which peak S/N ratio improvement is less than four times with two consecutive matched filters typically, are remedied to accomplish much better ratio enhancement approximately 16-folds when the noise frequency spectrum is modified between two matched filters.     

非靶向尿液代谢组学方法研究五味子治疗精尿病并发症作用机制

采用超高效液相色谱-串联四级杆飞行时间质谱联用技术(Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry,UPLC/Q-TOF-MS),结合主成分分析技术(Principal component analysis,PCA),进行了五味子治疗糖尿病并发症大鼠尿液代谢组学研究,对五味子治疗糖尿病并发症大鼠后尿液生物标志物变化进行了考察.采用UPLC/Q-TOF-MS方法分析了健康组、糖尿病并发症模型组和五味子给药组的大鼠尿液,采用偏最小二乘法-判别分析(Partial least squares discriminant analysis,PLS-DA)和正交偏最小二乘法-判别分析(Orthogonal partial least squares discriminant analysis,OPLS-DA)载荷图筛选,通过分析对各组分离贡献较大(VIP>1,P°0.05)化合物的串联质谱数据,再经Human Metabolome Data-base(HMDB)等数据库检索,进行质谱信息匹配,尿液中共鉴定出28种潜在生物标记物,其中正谱13种,负谱15种.这些生物标记物主要影响戊糖和葡糖醛酸相互转化通路、核黄素代谢、泛酸和CoA合成、精氨酸和脯氨酸代谢、肠内菌代谢、嘌吟代谢、Vc代谢胆酸合成、色氨酸代谢等通路,五味子从能量代谢、氨基酸代谢、肠内菌代谢、脂类代谢等各个角度发挥治疗糖尿病并发症的作用.从各个通路的相关生物功能分析,五味子治疗糖尿病肾病的作用较强,此外还具有降脂、抗氧化的功效.     

Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development

The application of liquid chromatography/mass spectrometry (LC/MS) followed by principal components analysis (PCA) has been successfully applied to the screening of rat urine following the administration of three candidate pharmaceuticals. With this methodology it was possible to differentiate the control samples from the dosed samples and to identify the components of the mass spectrum responsible for the separation. These data clearly show that LC/MS is a viable alternative, or complementary, technique to proton NMR for metabonomics applications in drug discovery and development.     

An improved pseudotargeted metabolomics approach using multiple ion monitoring with time-staggered ion lists based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry

Pseudotargeted metabolomics is a novel strategy integrating the advantages of both untargeted and targeted methods. The conventional pseudotargeted metabolomics required two MS instruments, i.e., ultra-high performance liquid chromatography/quadrupole-time- of-flight mass spectrometry (UHPLC/Q-TOF MS) and UHPLC/triple quadrupole mass spectrometry (UHPLC/QQQ-MS), which makes method transformation inevitable. Furthermore, the picking of ion pairs from thousands of candidates and the swapping of the data between two instruments are the most labor-intensive steps, which greatly limit its application in metabolomic analysis. In the present study, we proposed an improved pseudotargeted metabolomics method that could be achieved on an UHPLC/Q-TOF/MS instrument operated in the multiple ion monitoring (MIM) mode with time-staggered ion lists (tsMIM). Full scan-based untargeted analysis was applied to extract the target ions. After peak alignment and ion fusion, a stepwise ion picking procedure was used to generate the ion lists for subsequent single MIM and tsMIM. The UHPLC/Q-TOF tsMIM MS-based pseudotargeted approach exhibited better repeatability and a wider linear range than the UHPLC/Q-TOF MS-based untargeted metabolomics method. Compared to the single MIM mode, the tsMIM significantly increased the coverage of the metabolites detected. The newly developed method was successfully applied to discover plasma biomarkers for alcohol-induced liver injury in mice, which indicated its practicability and great potential in future metabolomics studies.     

Characterization of protein adducts formed by toxic alkaloids by nano‐scale liquid chromatography with mass spectrometry

Betel quid chewing is associated with cytotoxicity, genotoxicity and carcinogenicity in diseases such as oral cancer, liver cirrhosis, hepatocellular carcinoma and diabetes mellitus. Arecoline and arecaidine, which are the main alkaloids in the areca nut, are potential exposure biomarkers in habitual betel quid users. This study developed a method of detecting arecoline‐ and arecaidine‐protein adducts by mass spectrometry (MS). First, bovine serum albumin was used to predict and confirm the binding sites of proteins modified by arecoline or arecaidine. Cells were then treated with arecoline to identify new protein adducts after cellular metabolic processing. Finally, human plasma was used to model long‐term exposure to arecoline and arecaidine. Following isolation proteins were tryspin digested. The peptides afforded were separated and analyzed by nano‐scale liquid chromatography with MS using an LTQ Orbitrap mass spectrometer. The experimental findings showed that cysteine is the predominant amino acid in protein adduct formation. The goal of this study was to establish a screening platform for identifying novel protein adducts that form covalent bonds with arecoline or arecaidine. Use of this strategy to survey new protein‐toxic adducts may help to identify novel biomarkers of betel nut exposure. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Metabolomics Coupled with Pattern Recognition and Pathway Analysis on Potential Biomarkers in Liver Injury and Hepatoprotective Effects of Yinchenhao

Metabolomics can provide an opportunity to develop the systematic analysis of the metabolites in biological samples and has been increasingly applied to discovering and identifying biomarkers and perturbed pathways. It enables us to better understand the metabolic pathways which can clarify the mechanism of traditional Chinese medicines (TCM). Yinchenhao (YCH, Artemisia annua L), a famous TCM plant, has been used clinically for more than a thousand years to relieve liver diseases in Asia, and its mechanisms are not still completely clear. Here, metabolomic techniques may provide additional insight, and our investigation was designed to assess the effects and possible mechanisms of YCH on α-naphthylisothiocyanate (ANIT)-induced liver injury. Metabolite profiling was performed by ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) combined with pathway analysis and pattern recognition approaches including independent component analysis (ICA) and partial least squares-discriminant analysis (PLS-DA). Biochemistry test was also performed for the liver tissue and plasma samples. The changes in metabolic profiling were restored to their baseline values after YCH treatment according to the ICA score plots. Of note, YCH has a potential pharmacological effect through regulating multiple perturbed pathways to normal state, correlating well to the assessment of biochemistry test. Five different potential biomarkers in the positive mode contributing to the treatment of YCH were discovered. Pathway analysis showed that these metabolites were associated with perturbations in pyrimidine metabolism, primary bile acid biosynthesis, and propanoate metabolism, which may be helpful to further understand the action mechanisms of YCH. It showed that changed biomarkers and pathways may provide evidence to insight into drug action mechanisms and drug discovery.