基于UPLC-Q/TOF MS技术的痛风患者血清代谢组学研究

采用超高效液相色谱-飞行时间质谱联用技术（UPLC-Q/TOF MS）对恩施土家族苗族自治州60例健康志愿者（对照组）和65例痛风患者（痛风组）的血清样本建立代谢图谱,基于主成分分析及正交偏最小二乘判别分析对所得数据进行模式识别,并结合变量权重投影分析及火山图筛选出痛风患者的血清代谢标志物。通过数据分析和数据库检索,共筛选出63种差异代谢物,其中27种代谢物显著上调（P <0.05）,36种代谢物显著下调（P <0.05）,主要包括甘油磷脂类、氨基酸类及胆碱等成分。首先,对以上差异代谢物进行受试者工作特征曲线（ROC）分析,其中曲线下面积（AUC）大于0.8的14种代谢物是诊断效能较好的代谢物;然后对筛选的63种差异代谢物进行代谢通路富集分析,以Impact> 0.1且P <0.05为标准,得到影响最大的代谢通路主要有甘油磷脂代谢、醚性脂质代谢、亚油酸代谢、半胱氨酸和蛋氨酸代谢、花生四烯酸代谢及戊糖和葡萄醛酸的互相转化等。综上,痛风患者和健康对照人群的血清代谢水平有明显差异,差异代谢物的鉴定为痛风的发病机制和早期筛查提供了实验依据。     

超高效液相色谱-质谱联用技术在药物肝损伤代谢组学研究中的应用北大核心CSCD

药物引起的肝损伤是全世界关注的健康问题,药源性肝损伤的早期诊断仍然是临床治疗肝损伤的一大难题。本研究中,首先利用对乙酰氨基酚、四氯化碳、大黄素、雷公藤甲素和马兜铃酸构建不同类型的肝细胞损伤模型,利用超高效液相色谱-质谱联用技术(UPLC-MS)分别得到正常组和损伤组的细胞代谢轮廓谱。然后利用模式识别构建分类模型,筛选肝损伤相关的差异代谢物。结果显示,不同类型肝损伤在肝细胞代谢谱上可以被区分开,最终鉴定出14种差异代谢物。损伤组肝细胞经保肝阳性药联苯双酯干预后,差异代谢物水平均趋向于正常组,在一定程度上验证了差异标志物与肝损伤的相关性。实验结果表明细胞代谢组学是研究药物肝损伤的有效工具之一。     

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。     

黄芪黄酮干预脾虚水湿不化大鼠血浆代谢组学研究

采用高效液相色谱结合飞行时间质谱检测正常大鼠、脾虚水湿不化证大鼠及黄芪黄酮组分干预后大鼠血浆内源性代谢物变化,获取大鼠血浆代谢图谱,研究黄芪黄酮组分干预脾虚水湿不化证的作用机制.采用高脂低蛋白饮食加负重游泳力竭建立脾虚水湿不化证大鼠模型,选择Halo C18色谱柱,流动相为0.05％甲酸-水与0.05％甲酸-乙腈,梯度洗脱,利用液相色谱-串联质谱分析测定大鼠血浆样品.利用主成分分析法对造模前后大鼠血浆样本进行代谢轮廓分析,结合变量投影重要性及显著性分析等筛选对分组贡献最大的差异标志物及相关通路,阐明药物的作用机制.结果表明, 黄芪黄酮组代谢轮廓异于模型组,而接近于正常组,共鉴定出了包含甘油磷脂类、鞘酯类、氨基酸类等在内的11种潜在生物标志物,其代谢通路包括三羧酸循环、甘油磷脂代谢、脂肪酸代谢及氨基酸代谢等,主要涉及体内能量代谢和脂肪代谢.黄芪黄酮干预脾虚水湿不化证大鼠后, 宏观指标(如体重、自主活动)和微观指标(如代谢标志物、血脂)均明显回调,表明黄芪黄酮可能主要通过调节能量代谢、脂肪代谢等途径而发挥健脾利水作用.     

超高效液相色谱-质谱联用技术在药物肝损伤代谢组学研究中的应用

药物引起的肝损伤是全世界关注的健康问题,药源性肝损伤的早期诊断仍然是临床治疗肝损伤的一大难题。本研究中,首先利用对乙酰氨基酚、四氯化碳、大黄素、雷公藤甲素和马兜铃酸构建不同类型的肝细胞损伤模型,利用超高效液相色谱-质谱联用技术(UPLC-MS)分别得到正常组和损伤组的细胞代谢轮廓谱。然后利用模式识别构建分类模型,筛选肝损伤相关的差异代谢物。结果显示,不同类型肝损伤在肝细胞代谢谱上可以被区分开,最终鉴定出14种差异代谢物。损伤组肝细胞经保肝阳性药联苯双酯干预后,差异代谢物水平均趋向于正常组,在一定程度上验证了差异标志物与肝损伤的相关性。实验结果表明细胞代谢组学是研究药物肝损伤的有效工具之一。     

基于液相色谱与质谱联用的代谢组学及磷脂轮廓分析在糖代谢异常研究中的应用

糖代谢异常由于其发病率的升高和影响人类的生活质量而日益受到科学研究者的关注。实验中利用液相色谱与质谱(LC-MS)联用技术对糖代谢异常分别进行了代谢组学和磷脂轮廓分析,研究了糖代谢异常中的两个阶段——空腹血糖受损(IFG)和初诊糖尿病(NDD)的代谢差异情况。首先从LC-MS采集到血浆中代谢组学分析及磷脂轮廓分析的原始谱图,通过软件的峰匹配等步骤得到峰表,之后利用多种统计分析方法进行数据分析,通过正交校正的偏最小二乘法(OSC-PLS)对样品进行分型,根据模型的变量重要因子(VIP)、显著性差异检验结果等筛选出差异性代谢物。结果显示: NDD组比IFG组与对照组(N组)比较存在更明显的代谢差异,发生变化的化合物主要为游离脂肪酸、溶血磷脂酰胆碱、磷脂酰乙醇胺、鞘磷脂和磷脂酰胆碱等。     

基于人工智能技术的烟气暴露大鼠代谢生物标志物筛选方法研究

该研究将主成分分析、偏最小二乘判别分析等多元统计分析方法用于烟草血浆、尿液和肺组织代谢组学数据的分析,以揭示暴露于不同烟气中大鼠血浆、尿液和肺组织中内源性生物标志物的整体变化情况,筛选潜在生物标志物;将血样、尿样和肺组织代谢轮廓谱分析得到的生物标志物进行整合,运用神经模糊网络模型对标志物进行缩减,并用人工神经网络评价模型预测能力,确定烟气暴露不同时间(7,14,30 d)以及不同烟气暴露对大鼠内源性代谢物变化影响"因果效应"密切相关的关键生物标志物群,明确不同烟气对大鼠机体损伤机制的异同。     

气相色谱-质谱法结合偏最小二乘-判别分析用于血清和血浆中代谢物的比较分析

建立了肟化-硅烷化衍生血清和血浆中代谢物的方法,结合气相色谱-质谱法,可实现有机酸、氨基酸、脂肪酸和胆固醇等多类代谢物的同时分析测定。方法学考察结果表明,该方法准确可靠,重复性、精密度和回收率良好,满足血清和血浆样本代谢物分析的要求。利用建立的方法,对40个健康人血清样本和34个健康人血浆样本中的代谢物进行对比分析。结果表明,吲哚-3-乙酸是血浆中特有的代谢物;共有的39个代谢物中,有32种在血清和血浆中的含量有显著性差异。采用偏最小二乘-判别分析(PLSDA)建立血清和血浆的判别模型,并筛选出了5种区分血清和血浆的重要代谢物,即磷酸、异亮氨酸、2,3,4-三羟基丁酸、柠檬酸和硬脂酸。研究结果为代谢组学研究中血清和血浆样本的选择提供了依据。     

高脂与维生素D缺乏饮食诱导的2型糖尿病小鼠血清和肝脏代谢组学研究

采用高脂与维生素D缺乏(VDD)饮食长期(24周)喂养小鼠,诱导其形成2型糖尿病(T2DM),通过小鼠血清和肝脏代谢组学分析探究了T2DM发生、发展的代谢物和代谢通路变化机制.实验收集小鼠血清和肝脏样品,通过气相色谱-质谱联用技术和硅烷化衍生方法分析得到血清和肝脏代谢轮廓;利用正交偏最小二乘判别分析和非参数检验筛选血清和肝脏代谢组中具有显著性差异的代谢标志物,发现血清样品中乳酸、丙氨酸、甘油、苏氨酸和葡萄糖含量在高脂+VDD小鼠中显著升高,肝脏样品中乳酸、核糖、果糖、葡萄糖、油酸和棕榈酸含量在高脂+VDD小鼠中显著升高.本文还进行了血清和肝脏代谢轮廓整体分析和代谢通路探索,发现高脂+VDD小鼠中三羧酸循环、糖异生、氨基酸以及脂质代谢通量均显著增强,这些代谢路径相互影响共同促进T2DM的发生和发展.本文通过饮食诱导小鼠形成T2DM,得到血清和肝脏代谢物及其代谢通路的变化关系,可为T2DM诊断提供参考信息.     

气相色谱-质谱和液相色谱-质谱联用方法用于口腔癌代谢组学分析

口腔癌的发病率占全身恶性肿瘤的第6位,正确区分正常状态与良性和恶性口腔肿瘤,是恰当选择治疗方案的关键所在。本研究中,首先利用液相色谱-质谱和气相色谱-质谱联用方法分别得到健康人、良性口腔肿瘤患者和恶性口腔肿瘤患者血浆、尿液和唾液的代谢轮廓,然后应用正交信号校正的偏最小二乘法进行多变量统计分析。结果表明健康人、良性肿瘤患者和恶性肿瘤患者在血浆、尿液和唾液等3种体液代谢中都可以被区分开,而且找到和鉴定出19个重要差异代谢物。相关代谢通路分析显示,与健康人相比,良性和恶性口腔肿瘤患者都存在能量代谢紊乱和脂类代谢失衡的现象,但恶性口腔肿瘤患者还表现出三羧酸循环和肌醇代谢异常,这为临床诊断及治疗提供了重要信息。     

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.     

Plasma metabolomic profile and potential biomarkers for missed abortion

A missed abortion (MA) is an in utero death of the embryo or fetus before the 20th week of gestation with retained products of conception, and this condition is currently common in China. In order to discover novel biomarkers for MA, ultrahigh performance liquid chromatography was applied to study plasma metabolite profiles for 33 patients with MA and 29 control subjects. Thirty‐seven differential plasma metabolites were found to discriminate between the two groups in the initial cohort (15 subjects with MA and 15 healthy controls). The feasibility of using these potential biomarkers to predict MA was further evaluated in the validation cohort (18 subjects with MA and 14 healthy controls) and 15 had an area under the receiver operating characteristic curve of >0.80, making them satisfactory. Tryptophan metabolism and sphingolipid metabolism were identified as important potential target pathways for MA using metabolic pathway impact analysis. Furthermore, three of the 15 satisfactory metabolites (glyceric acid, indole and sphingosine) were combined to establish a predictive model with 100% sensitivity and 100% specificity in the validation cohort. Taken together, these results suggest that MA results in significant disturbance of metabolism and those various novel biomarkers have satisfactory diagnostic and predictive power for MA.     

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.     

Identification of differential metabolic characteristics between tumor and normal tissue from colorectal cancer patients by gas chromatography–mass spectrometry

Colorectal cancer (CRC) is one of the most common human malignancies and encompasses cancers of the colon and rectum. Although the gold‐standard colonoscopy screening method is effective in detecting CRC, this method is invasive and can result in severe complications for patients. The purpose of this study was to determine differences in metabolites between CRC and matched adjacent nontumor tissues from CRC patients, to identify potential biomarkers that may be informative and developed screening methods. Metabolomic analysis was performed on clinically localized CRC tissue and matched adjacent nontumor tissue from 20 CRC patients. Unsupervised analysis, supervised analysis, univariate analysis and pathway analysis were used to identify potential metabolic biomarkers of CRC. The levels of 25 metabolites in CRC tissues were significantly altered compared with the matched adjacent nontumor tissues. Four metabolites (lactic acid, alanine, phosphate and aspartic acid) demonstrated good area under the curve of receiver‐operator characteristic with acceptable sensitivities and specificities, indicating their potential as important biomarkers for CRC. Alterations of amino acid metabolism and enhanced glycolysis may be major factors in the development and progression of CRC. Lactic acid, alanine, phosphate, and aspartic acid could be effective diagnostic indicators for CRC.     

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.     

Liquid chromatography/mass spectrometry for metabonomics investigation of the biochemical effects induced by aristolochic acid in rats: the use of information-dependent acquisition for biomarker identification

The toxic effects of oral administrations of nephrotoxic and carcinogenic aristolochic acid (AA) to male Sprague-Dawley rats were investigated by using high-performance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometer. Analysis of the urine and plasma samples revealed distinct changes in the biochemical patterns in the AA-dosed rats. After peak finding and alignment, principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were used for multivariate data analysis. Potential biomarkers were studied by high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) analyses. The MS/MS spectra of all endogenous metabolites satisfying the pre-defined criteria were acquired in a single information-dependent acquisition (IDA) analysis, demonstrating that IDA was an efficient approach for structural elucidation in metabonomic studies. Citric acid and a glucuronide-containing metabolite were observed as potential biomarkers in rat urine. A significant increase in plasma creatinine concentration was also observed in the AA-dosed rats, which indicated that AA induced an adverse effect on the renal clearance function.     

缺血性脑卒中患者MCA血流速度及脉动指数变化的相关分析

目的探讨并分析缺血性脑卒中患者MCA血流速度及脉动指数的变化。方法选取2016年1月—2017年1月就诊于佳木斯大学附属第一医院神经三科门诊及住院部的120例缺血性脑卒中住院患者,其中男性56例,女性64例。使用经颅多普勒超声诊断仪探查患者双侧大脑中动脉,测出患者大脑中动脉的血流速度和脉动指数。比较所有患者患侧与健侧的大脑中动脉血流速度和脉动指数变化。结果所有患者患侧大脑中动脉收缩期最快血流速度(t=15.63,P﹤0.05)、舒张期最快血流速度(t=10.61,P﹤0.05)明显小于健侧;而平均血流速度患侧与健侧比较无统计学意义(t=0.58,P0.05);并且大脑中动脉供脉动指数值明显高于健侧(t=3.54,P﹤0.05)。结论缺血性脑卒中患者大脑中动脉供血区脑梗死,临床进行TCD观察患者大脑中动脉血流速度及脉动指数的变化的临床诊疗,预判病情变化有指导意义。     

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.