Ultrafast gas chromatography coupled to electronic nose to identify volatile biomarkers in exhaled breath from chronic obstructive pulmonary disease patients: A pilot study

An analytical method to identify volatile organic compounds (VOCs) in the exhaled breath from patients with a diagnosis of chronic obstructive pulmonary disease (COPD) using a ultrafast gas chromatography system equipped with an electronic nose detector (FGC eNose) has been developed. A prospective study was performed in 23 COPD patients and 33 healthy volunteers; exhalation breathing tests were performed with Tedlar bags. Each sample was analyzed by FCG eNose and the identification of VOCs was based on the Kovats index. Raw data were reduced by principal component analysis (PCA) and canonical discriminant analysis [canonical analysis of principal coordinates (CAP)]. The FCG eNose technology was able to identify 17 VOCs that distinguish COPD patients from healthy volunteers. At all stages of PCA and CAP the discrimination between groups was obvious. Chemical prints were correctly classified up to 82.2%, and were matched with 78.9% of the VOCs detected in the exhaled breath samples. Receiver operating characteristic curve analysis indicated the sensitivity and specificity to be 96% and 91%, respectively. This pilot study demonstrates that FGC eNose is a useful tool to identify VOCs as biomarkers in exhaled breath from COPD patients. Further studies should be performed to enhance the clinical relevance of this quick and ease methodology for COPD diagnosis.     

Development and validation of a gas chromatography/mass spectrometry metabonomic platform for the global profiling of urinary metabolites

This paper presents a simple and reliable gas chromatography/mass spectrometry (GC/MS) method for the metabonomic analysis of human urine samples. The sample preparation involved the depletion of excess urea via treatment with urease and subsequent protein precipitation using ice-cold ethanol. An aliquot of the mixture was separated, dried, trimethylsilyl (TMS)-derivatized and 1.0 microL of the derivatized extract was injected into the GC/MS system via split injection (1:10). Approximately 150 putative metabolites belonging to different chemical classes were identified from the pooled human urine samples. All the identified metabolites were selected to evaluate precision and stability of the GC/MS assay. More than 95% of the metabolites demonstrated good reproducibility, with intra-day and inter-day precision values below 15%. Metabolic profiling of 53 healthy male and female urine samples in combination with pattern recognition techniques was performed to further validate the GC/MS metabolite profiling assay. Principal component analysis (PCA) followed by orthogonal partial least squares analysis (OPLS) revealed differences between urinary metabolite profiles of healthy male and female subjects. This validated GC/MS metabolic profiling method may be further applied to the metabonomic screening of urinary biomarkers in clinical studies. Copyright (c) 2008 John Wiley & Sons, Ltd.     

慢性心功能衰竭患者尿液的代谢组学研究

采用基于液相色谱-质谱联用的方法对慢性心力衰竭(Chronic heart failure, CHF)患者和正常对照(Control)人群的尿液进行分析, 筛选慢性心力衰竭患者尿液中的差异代谢物, 研究其发病机制, 并为临床治疗提供科学依据.选择15个慢性心力衰竭患者(年龄(62.27±3.14)岁)及15个正常人(年龄(65.41±4.63)岁), 采用高分辨度快速液相色谱-四极杆-飞行时间串联质谱(RRLC-QTOF/MS)技术对尿液代谢物进行分析, 采用主成分分析(PCA)对两组代谢物进行分类, 并筛选潜在生物标记物;运用偏最小二乘判别分析法(PLS-DA)建模, 考察生物标记物对疾病筛选的预测能力.研究结果表明, CHF组和Control组尿液代谢物谱能得到很好的区分, 发现并鉴定了2种潜在生物标记物尿苷及丙氨酰色氨酸, 提示嘧啶代谢和色氨酸代谢可能在心力衰竭发生发展中有重要作用.     

The Influence of Smoking Status on Exhaled Breath Profiles in Asthma and COPD Patients

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver–operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66–0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43–0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49–0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50–0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.     

小儿肺炎患者尿液的代谢组学初步研究

利用液相色谱-质谱联用法对小儿肺炎( Childhood pneumonia, CP)患者和健康儿童( Healthy control)的尿液进行分析,发现小儿肺炎患者尿液中的潜在标记物,为其发病机制及早期筛查提供科学依据。筛选10例小儿肺炎患者(age 47.72±2.35 months)及10例健康儿童(age 46.65±1.97 months)尿液样本,采用快速高分辨液相色谱四极杆-飞行时间质谱联用( RRLC-Q TOF/MS)技术对其尿液代谢物进行分析,通过主成分分析方法( PCA)对两组代谢物进行分类,并发现潜在生物标记物。 RRLC-Q TOF/MS检测表明,CP组和Healthy Control组尿液代谢物图谱能得到很好的区分,并鉴定了5种生物标记物,提示嘌呤代谢、氨基酸代谢可能在小儿肺炎发生发展中有重要作用。     

Urinary Metabonomics Study of Heart Failure Patients with HILIC and RPLC Separation Coupled to TOF–MS

In this study, urinary metabolic profiles of patients with heart failure (HF) and healthy individuals were analyzed by LC-TOF–MS. Both reversed-phase chromatography and hydrophilic interaction chromatography were used to separate the endogenous metabolites in urine. Partial least-squares to latent structure-discriminant analysis was used for discriminating HF patients from healthy persons and the selection of potential biomarkers. The results suggested that the combination of LC–MS and multivariate statistical analysis could be used for HF diagnosis. The MS/MS experiments were carried out to identify the potential biomarkers which are important for the contribution to the discrimination. As a result, 12 potential biomarkers for HF were identified and the related metabolic pathways were studied.     

Exploratory analysis of human urine by LC–ESI-TOF MS after high intake of olive oil: understanding the metabolism of polyphenols

Olive oil polyphenols have important biological properties which closely depend on their bioavailability; it is, therefore, essential to understand how polyphenols are absorbed, metabolized, and eliminated from the body. An analytical method based on rapid-resolution liquid chromatography (RRLC) coupled with mass spectrometric detection with a time-of-flight analyzer (RRLC–ESI-TOF MS) has been developed for analysis of the main olive oil phenolic compounds and their metabolites in human urine. Urine samples from ten healthy volunteers were collected before and 2, 4, and 6 h after intake of 50 mL extra-virgin olive oil. The proposed method includes liquid–liquid extraction with ethyl acetate, which provides extraction recoveries of the phenolic compounds studied between 35 and 75% from spiked urine samples. Good repeatability was obtained—the relative standard deviations (RSDs) of peak areas in intra-day and inter-day studies were 4.3 and 6.5%, respectively. Statistical studies enabled us to discriminate between urine samples before and after intake, and facilitated the search for m/z values enabling this discrimination. Based on the very accurate mass information and the isotopic pattern provided by the TOF MS analyzer, together with other available information, ten of these biomarkers and more than 50 metabolites, obtained through phase I and phase II biotransformation reactions, were tentatively identified. Additionally, kinetic studies were conducted on the metabolites identified as possible biomarkers; for most of the compounds concentrations were maximum in the first two hours.     

Urine biomarkers discovery by metabolomics and machine learning for Parkinson's disease diagnoses

Parkinson’s disease(PD) is a complex neurological disorder that typically worsens with age. A wide range of pathologies makes PD a very heterogeneous condition, and there are currently no reliable diagnostic tests for this disease. The application of metabolomics to the study of PD has the potential to identify disease biomarkers through the systematic evaluation of metabolites. In this study, urine metabolic profiles of 215 urine samples from 104 PD patients and 111 healthy individuals were ass...     

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.     

基于气相色谱-质谱联用和图模型分析的脂代谢异常患者血浆代谢组学研究

利用气相色谱-质谱联用技术(GC-MS)和图模型分析方法,寻找脂代谢异常患者可能的血浆代谢标志物群。采用GC-MS技术对37例脂代谢异常患者和10例健康人的血浆样品进行分析,得到血浆代谢物的表达谱。偏最小二乘-判别分析(Partial least squares-discriminant analysis,PLS-DA)得分图可区分脂代谢异常患者与健康人,运用PLS-DA载荷图及t检验发现有9个代谢物在两组间存在显著性差异。经NIST谱库检索,它们分别为缬氨酸、甘氨酸、丙氨酸、焦谷氨酸、葡萄糖醛酸、半乳糖、甘露糖、亚油酸和甘油。在脂代谢异常患者血浆中,除甘油浓度显著高于健康人外,其余8种代谢物浓度均明显低于健康人。图模型分析结果发现这些代谢物与脂代谢异常临床常用诊断指标之间具有很好的相关性。它们可能是脂代谢异常疾病早期诊断和预后新的特异性代谢标志物群。     

Urinary metabolomic study of systemic lupus erythematosus based on gas chromatography/mass spectrometry

Systemic lupus erythematosus (SLE) is an autoimmune disease with heterogeneous organ and system manifestations. In this study, urinary metabolic alterations related to SLE were investigated by performing gas chromatography/mass spectrometry (GC/MS) based metabolomics and multivariate statistical analysis. Patients with SLE and healthy controls could be clearly differentiated in view of the metabolic abnormity in urine. Among 70 identified endogenous metabolites, 23 metabolites were dramatically increased in SLE patients, which involved in several key metabolic pathways including energy metabolism, nucleotide metabolism, oxidative stress and gut‐microbiome‐derived metabolism. This noninvasive and GC/MS‐based metabolomic technique is a promising and potent strategy for identifying novel biomarkers and understanding pathogenesis of SLE. Copyright © 2016 John Wiley & Sons, Ltd.     

Ultra high performance liquid chromatography tandem mass spectrometric detection of glucuronides resistant to enzymatic hydrolysis: Implications to doping control analysis

Controversial results have been reported in the literature regarding the behavior of two testosterone (T) metabolites (3α-glucuronide-6β-hydroxyandrosterone and 3α-glucuronide-6β-hydroxyetiocholanolone) excreted after T administration. Due to their potential as biomarkers of T misuse, a UHPLC–MS/MS method for the direct quantification of these glucuronides was developed and validated. In addition, the main phase II metabolites of T that compose the steroid profile used for doping control purposes (glucuronides of T, epitestosterone, androsterone and etiocholanolone) were included. The method was found to be linear and with suitable LODs and LOQs for all metabolites. The average accuracies were between 86% and 120%, the RSDs for the intra- and inter-day precision were below 15% and 25% respectively. The method showed low matrix effect. Samples obtained before and after the administration of T were analyzed by both the developed UHPLC–MS/MS method and the GC–MS/MS method currently used by anti-doping laboratories. Relevant disagreements between the results obtained for 3α-glucuronide-6β-hydroxyandrosterone and 3α-glucuronide-6β-hydroxyetiocholanolone quantitation were observed. These markers seemed to be more suitable for the screening of T misuse when detected by UHPLC–MS/MS. These discrepancies were further investigated in 50 urine samples from healthy volunteers. The two methods gave highly correlated results for all metabolites that are currently included in the athlete's steroid profile confirming the reliability of the UHPLC–MS/MS method. However, the quantification of 3α-glucuronide-6β-hydroxyandrosterone and 3α-glucuronide-6β-hydroxyetiocholanolone, was only possible by using the UHPLC–MS/MS method since three interfering compounds were observed when performing the GC–MS/MS analysis with the most intense ion transitions. These results confirm the potential of the resistant glucuronides as biomarkers of T misuse. Additionally, they suggest that previously reported reference ranges for these metabolites should be reevaluated.     

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.     

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.     

Nontargeted urine metabolomics analysis of the protective and therapeutic effects of Citri Reticulatae Chachiensis Pericarpium on high-fat feed-induced hyperlipidemia in rats

In this study, we focused on studying the changes in urine metabolites in hyperlipidemic rats using ultra-performance liquid chromatography coupled with quadrupole time-of-fight mass spectrometry (UPLC–Q-TOF/MS) and metabolomics, as well as the effect of Citri Reticulatae Chachiensis Pericarpium (CRCP) on hyperlipidemia. These urine samples were examined by UPLC–Q-TOF/MS to obtain MS data. The MS data were analyzed by principal component analysis and partial least squares-discriminant analysis to identify the differential metabolites. CRCP reduced the body weight and levels of triglycerides, total cholesterol and low-density lipoprotein cholesterol and abnormally decreased high-density lipoprotein cholesterol in hyperlipidemic rats, which were significantly raised by a high-fat diet. Twenty-seven potential biomarkers were identified within the complex sample matrix of urine. Fourteen biomarkers increased in the hyperlipidemia rats compared with normal rats. Meanwhile, 13 biomarkers decreased. CRCP reversed abnormal changes in biomarkers, including 5-l -glutamyl-taurine, 5-aminopentanoic acid, cis-4-octenedioic acid and 2-octenedioic acid. These biomarkers show that hyperlipidemia is related to the metabolic pathways of taurine and hypotaurine metabolism, fatty acid biosynthesis , and arginine and proline metabolism . CRCP mainly prevents hyperlipidemia by intervening in these metabolic pathways.     

Searching for Potential Markers of Glomerulopathy in Urine by HS-SPME-GC×GC TOFMS

Volatile organic compounds (VOCs) exiting in urine are potential biomarkers of chronic kidney diseases. Headspace solid phase microextraction (HS-SPME) was applied for extraction VOCs over the urine samples. Volatile metabolites were separated and identified by means of two-dimensional gas chromatography and time of flight mass spectrometry (GC × GC TOF MS). Patients with glomerular diseases (n = 27) and healthy controls (n = 20) were recruited in the study. Different VOCs profiles were obtained from patients and control. Developed methodology offers the opportunity to examine the metabolic profile associated with glomerulopathy. Four compounds found in elevated amounts in the patients group, i.e., methyl hexadecanoate; 9-hexadecen-1-ol; 6,10-dimethyl-5,9-undecadien-2-one and 2-pentanone were proposed as markers of glomerular diseases.     

Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach

Metabolomics represents an emerging and powerful discipline concerned with the comprehensive analysis of small molecules and provides a powerful approach to discover biomarkers in biological systems. Recent development of biomarkers for diagnosis and therapeutic monitoring of liver-stagnation and spleen-deficiency syndrome (LSS)-type disease remains challenging. This study was undertaken to discover novel potential biomarkers for the non-invasive early diagnosis of human LSS. Urine samples which are potentially a rich source of metabolites were collected from patients with LSS, together with healthy control samples. Metabolite profiling was performed by ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry (UPLC-Q-TOF-HDMS) in conjunction with multivariate data analysis and ingenuity pathway analysis that were used to select the metabolites to be used for the non-invasive diagnosis of LSS. Twelve urinary differential metabolites contributing to the complete separation of LSS patients from matched healthy controls were identified involving several key metabolic pathways such as pentose and glucuronate interconversions, ascorbate, aldarate, cysteine, methionine, tyrosine, tryptophan, amino sugar and nucleotide sugar metabolism. More importantly, of the 12 differential metabolites, 4 metabolite markers, prolylhydroxyproline, l-homocystine, 2-octenoylcarnitine and α-N-phenylacetyl-l-glutamine, were effective for the diagnosis of human LSS, with an achieved sensitivity of 93.0%. These results demonstrate that robust metabolomics has the potential as a non-invasive strategy and promising screening tool to evaluate the potential of these metabolites in the early diagnosis of LSS patients and provides new insight into pathophysiological mechanisms.     

Some aspects of quantification of neutral steroid metabolites in body fluids of healthy and uremic subjects by capillary gas chromatography

Neutral steroid metabolites enriched from urine and hemofiltrate were identified by gas chromatography/mass spectrometry and quantified by capillary gas chromatography. This study included 20 healthy controls and 37 uremic patients. Before enrichment of steroids from biological material, the standard deviation of the workup procedure and subsequent derivatization into the trimethylsilyl-enol-trimethylsilyl ethers was tested and found to be 2–5% in urine and 12–17% in the more complicated workup procedure of hemofiltrate, but essentially smaller than the biological standard deviation. Compared to the 24 h urinary excretion rates of controls, the excretion rates of androsterone, etiocholanolone, and corticoid metabolites were significantly lower in uremic body fluids, while those of 11-oxygenated androstanolones, degradation products of corticoids, were enhanced in uremic urine. The ratio of corticoid metabolites to 11-oxygenated androstanolones in urine of nondialyzed uremics correlated significantly with their plasma creatinine levels.     

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.     

Lysophosphatidylcholine Biomarkers of Lung Cancer Detected by Ultra-performance Liquid Chromatography Coupled with Quadrupole Time-of-flight Mass Spectrometry

Centrifugal ultrafiltration after methanol extraction of whole plasma was used as an optimal condition for the preparation of blood plasma before metabonomic studies. The plasma samples from 102 lung cancer patients and 34 healthy volunteers were prepared with this approach. With ultra-performance liquid chromatography(UPLC) coupled with quadrupole time-of-flight mass spectrometry(Q-TOF MS) analysis, the samples were investigated in order to find potential disease biomarkers. After data acquisition, orthogonal signal correction partial least squares models were built to differentiate the healthy volunteers from lung cancer patients and to identify metabolites that showed significantly different expression between the two groups. Several metabolite ions were identified as potential biomarkers according to the variable importance in the project(VIP) value in both ion modes. Five lysophosphatidylcholines were further identified as specifically lysoPC 16:0, isomer of lysoPC 16:0, lysoPC 18:0, lysoPC 18:1 and lysoPC 18:2. These results suggest that UPLC coupled with Q-TOF MS is an effective technique for the analysis of plasma metabolites in metabonomic studies.