基于质谱技术的代谢组学研究及其在中国的发展

代谢组学是关于生物系统代谢物组成及变化规律的科学,是系统生物学的重要组成部分.质谱技术是目前代谢组学研究中最主要的分析手段之一,广泛应用于代谢组学各个领域.本文阐述了基于质谱技术的代谢组学方法及其应用,重点介绍和评论了近年来我国在该领域取得的进步和成果,并对基于质谱技术的代谢组学研究目前存在的问题及未来的发展进行了分析与展望.     

脂组学及其研究进展

脂质的生物功能具有多样性,其代谢与多种疾病的发生、发展密切相关.脂质的分析量化对研究疾病发生机理和诊断治疗,以及医药研发有非常重要的生物学意义.分析技术的快速发展,特别是质谱及其联用技术的运用,促使脂质分析不断完善.脂组学就是对生物样本中脂质的整体分析,是代谢组学的重要组成部分,能够促进代谢组学的发展.本文就脂质的生物功能、脂质分析以及脂组学的研究现状作简要评述.     

代谢组学分析技术及数据处理技术

用于代谢组学数据获取的分析技术主要包括NMR、FT-IR、MS及其与色谱等联用技术.这些分析手段正在使代谢组学产生海量数据.处理、分析和管理这些数据需要专门的数学、统计和信息学知识和工具.主要综述了代谢组学中所采用的分析手段、由其所获取的多维代谢组学原始数据的处理方法,代谢组学所采用的数据标准及数据库技术、统计分析方法,以及相关方面的发展需求.     

色谱-质谱联用技术在代谢组学中的应用

代谢组学是对生物体受外部刺激所产生的小分子代谢产物的变化或其随时间的变化进行研究的一门学科,以实现对体液、细胞以及组织提取物等复杂的生物样本中所有代谢产物的定性和定量分析为研究目标。色谱-质谱联用技术在代谢组学的研究中已显示出极大的发展潜力。本文主要综述近年来代谢组学研究中涉及的色谱-质谱联用技术及其数据处理方法,重点介绍各种分离技术的特点及其在应用中的关键问题,并对其在代谢组学应用中的未来发展给予展望。     

基于液相色谱-质谱技术的代谢组学分析方法新进展

液相色谱-质谱联用技术是代谢组学研究领域的主要技术平台之一,近年来基于液相色谱-质谱联用技术的代谢组学分析方法获得了巨大发展。本文结合本研究组在代谢组学方面的研究成果,综述了近年来液相色谱-质谱联用技术在代谢组学分析方法方面的新进展,并对其发展前景进行了展望。综述引用文献41篇。     

基于核磁共振技术的代谢组学研究在口腔疾病中的进展

代谢组学是近年来发展起来的组学技术,越来越多地被用到生物技术各个领域。通过定性定量分析复杂生物样品中小分子代谢物的变化,反映生物体的生理病理状态。在口腔疾病研究中,代谢组学研究也获得了密切关注,它可以为临床研究提供重要参考信息。核磁共振技术可以无损伤、非侵入性地获取生物体的信息,使其在代谢组学研究中迅速发展。本文综述了基于核磁共振技术的代谢组学研究在口腔癌、牙周病、龋病等口腔疾病方面的应用,并对其发展趋势做出展望。     

液相色谱和质谱联用技术结合化学计量学应用于代谢组学的研究进展

代谢组学作为系统生物学的重要组成部分,已经成为继基因组学、转录组学、蛋白质组学之后兴起的一个新的组学研究热点。准确全面的检测生物体液中浓度较低的代谢物变化是进行代谢组学研究的基础。液相色谱和质谱联用(LC/MS)技术结合化学计量学很好地实现了对大量样品和微量代谢物的快速定性、定量分析,极大地推动了代谢组学的相关研究。本文综述了LC/MS与化学计量学相结合用于代谢组学研究的现状,并对后续的研究进行了展望。     

汞离子对细胞代谢通路影响的代谢组学

利用超高效液相色谱-四级杆飞行时间质谱（UPLC-Q-TOF-MS）技术对汞离子作用后细胞的代谢组学进行了研究,结合化学计量学方法,对代谢组学数据进行了多维统计分析.结果表明,与非汞离子作用组相比,汞离子作用组存在能量、磷脂、脂肪酸及氨基酸代谢异常,并发现16种有显著差异的生物标记物.进一步探讨了汞中毒的细胞代谢机理及细胞的应激保护.本研究建立的基于UPLC-Q-TOF-MS技术的细胞代谢组学快速分析方法可以对细胞在重金属作用后的代谢物变化进行轮廓分析.     

代谢组学及其研究进展

1　代谢组学的定义和特点　　近年来,随着人类基因组测序工作的完成,人们对生命过程的理解有了很大的提高,研究的热点转移到基因的功能和几个“组学”研究,包括研究核糖核酸(RNA)转录过程的转录组学、研究某个过程中所有蛋白及其功能的蛋白组学、研究代谢产物的变化及代谢途径的代谢组学。　　代谢组学作为一门新发展的技术,它是通过考察生物体系受刺激或扰动后(如将某个特定的基因变异或环境变化后)其代谢产物的变化或其随时间的变化,来研究生物体系的代谢途径的一种技术[1];它所关注的是相对分子质量为1000以下的小分子。代谢组学的代表…     

代谢组学及其在微生物领域的研究进展

代谢组学、基因组学和蛋白质组学是系统生物学研究的重要组成部分。本文在文献和作者本人研究的基础上,对代谢组学的产生和技术平台及其在环境微生物领域的研究进展进行了评述。     

Metabonomics Study of Heart Homogenates from Myocardial Infarction Rats Using Liquid Chromatography/Time of Flight Mass Spectrometry

In this study, a metabonomics analysis of heart homogenates from myocardial ischemic rats was performed by LC–TOF–MS. Hydrophilic interaction chromatography (HILIC) was used to separate the endogenous metabolites in heart homogenates. Partial least squares to latent structure-discriminant analysis (PLS-DA) was used for data analysis. Good separations were observed between the normal and model groups and 15 potential biomarkers were identified. The major disturbed metabolic pathways were purine metabolism, pyrimidine metabolism, urea cycle, and energy metabolism. The results demonstrated that a metabonomics approach based on HILIC-MS was useful for studying metabolic mechanism on target tissue of the myocardial infarction rat.

     

In vivo toxicity screening programs using metabonomics

Metabonomics is an emerging technology that enables rapid in vivo screening for toxicity, disease state, or drug efficacy. The technology combines the power of high-resolution nuclear magnetic resonance (NMR) techniques with statistical data analysis methods to rapidly evaluate the metabolic "status" of an animal. Complimentary to other profiling technologies like proteomics and genomics, metabonomics provides a fingerprint of the small-molecules contained in a given biofluid through the time course of a study. This article reviews the steps in implementing a metabonomics-based screening program from study design through data analysis. While metabonomics is still a relatively new technology in comparison to the other "omics", published results from metabonomics studies demonstrate its potential impact in the drug discovery process by enabling the incorporation of safety endpoints much earlier in the drug discovery process, reducing the likelihood (and cost) of later stage attrition.     

甲亢患者血清和尿液的核磁共振代谢组学研究

应用基于核磁共振(NMR)的代谢组学方法, 研究甲状腺功能亢进(简称甲亢)患者和健康人群的血清和尿液, 分析甲亢疾病的特征代谢物. 实验收集33个甲亢患者和17个健康志愿者的血清样品以及53个甲亢患者和58个健康志愿者的尿液样品, 采用多元统计分析方法研究甲亢组和对照组血清和尿液中的内源性代谢差异. 结果表明, 甲亢组血清中的胆碱、葡萄糖和三甲胺等物质的含量升高, 而VLDL, LDL和胆固醇等脂质以及乳酸、糖蛋白和丙氨酸等代谢物的含量下降; 甲亢组尿液中的葡萄糖、柠檬酸、牛磺酸以及肌氨酸等代谢物的含量升高, 而马尿酸、TMAO、甲酸和琥珀酸等代谢物的含量下降. 结果表明, 甲亢病不仅影响了糖类、脂类和蛋白质三大物质的代谢, 还对能量代谢、肝肠循环和肠道微生物等多个生理系统产生显著影响, 并且可能造成肝脏及肾脏等器官的损伤.     

Integrated GC-MS and LC-MS plasma metabonomics analysis of ankylosing spondylitis

Ankylosing spondylitis (AS) is a chronic inflammatory arthritis that predominantly affects the axial skeleton in adolescent patients. The natural history of the disease remains poorly characterized. In this study, we combined GC-MS and LC-MS techniques to evaluate the major metabolic changes in the plasma of AS patients in view of metabonomics. Univariate and multivariate analysis were employed for altered metabolite comparison and pattern recognition. Application of supervised partial least-squares discrminant analysis to either GC-MS or LC-MS data allowed accurate discrimination of AS patients from normal controls, demonstrating its potential diagnostic utilization. In addition, AS patients presented elevated plasma concentrations of proline, glucose, phosphate, urea, glycerol, phenylalanine and homocysteine but reduced levels of phosphocholines, tryptophan and a bipeptide - phenylalanyl-phenylalanine. In the context of their involved metabolic pathways, the identified metabolites were discussed accordingly. This investigation primarily proved that integrated chromatography-mass spectrometry and integrated uni- and multi-variate statistical analysis facilitated metabonomics to be a more promising tool in disease research.     

阳虚体质者血清和尿液的核磁共振代谢组学

采用基于核磁共振(NMR)的代谢组学方法研究了中医阳虚体质及平和体质(正常对照)的血清和尿液, 分析了阳虚体质的特征代谢物. 实验收集阳虚体质及平和体质各30人的血清及尿液样品, 采用多元统计分析方法研究了阳虚质组和对照组血清和尿液中的内源性代谢差异. 结果表明, 阳虚质血清中乳酸、 极低密度脂蛋白/低密度脂蛋白、N-乙酰糖蛋白、脂肪酸及不饱和脂肪酸的含量降低, 谷氨酰胺、葡萄糖、磷脂酰胆碱及高密度脂蛋白的含量增多; 尿液中肌酐的含量降低, 乳酸、二甲胺、柠檬酸及马尿酸的含量增多. 阳虚体质的潜在生物标志物的发现从代谢组学角度为个体差异提供了新的依据. 阳虚体质与平和体质存在能量代谢、脂代谢及糖代谢的差异以及相关脏腑功能的改变.     

Metabonomics study of the effects of traditional Chinese medicine formula Ermiaowan on hyperuricemic rats

To explore the global mechanism of Ermiaowan on hyperuricemia regulation, the holistic function of Ermiaowan for hyperuricemia in rats was firstly assessed by the urinary metabonomics method which was based on ultra‐high performance liquid chromatography with electrospray ionization quadrupole time‐of‐flight mass spectrometry. The urinary targeted metabonomics approach combined with the serum biochemical analysis and histological assay was conducted to verify the research result. As a result, the significant differences in metabolic profiles were observed from Ermiaowan‐treated group, model group, and healthy control group by using multivariate statistical approaches. Twenty therapeutic related metabolites were identified in response to the therapeutic effects of Ermiaowan, which were mainly associated with purine metabolism, pyrimidine metabolism, tryptophan metabolism, tricarboxylic acid cycle, and tyrosine metabolism. In addition, the urinary targeted metabonomics study showed that Ermiaowan can better restore the disturbed pathways than Phellodendri cortex and Atractylodis rhizome. The biochemical assay and histopathological assay confirmed that Ermiaowan could significantly reduce uric acid and fibrosis areas of kidney. These results provided new insights into the mechanism of Ermiaowan on hyperuricemia.     

Beyond blood sugar: the potential of NMR-based metabonomics for type 2 human diabetes,and the horse as a possible model

Metabonomic analysis is a powerful tool for identifying and characterizing metabolic disorders, for example type 2 diabetes and the metabolic syndrome. Nuclear magnetic resonance (NMR) spectroscopy is an essential tool for such analysis, with special benefits. The review assesses the current status and potential of NMR-based metabonomics of type 2 diabetes. The horse is proposed as a possible model for studying this condition and disease. Some examples are shown of horse blood analyses by NMR.     

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。     

Partial least‐squares discriminant analysis optimized by particle swarm optimization: application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics

The complexity of metabolic profiles makes chemometric tools indispensable for extracting the most significant information. Partial least‐squares discriminant analysis (PLS‐DA) acts as one of the most effective strategies for data analysis in metabonomics. However, its actual efficacy in metabonomics is often weakened by the high similarity of metabolic profiles, which contain excessive variables. To rectify this situation, particle swarm optimization (PSO) was introduced to improve PLS‐DA by simultaneously selecting the optimal sample and variable subsets, the appropriate variable weights, and the best number of latent variables (SVWL) in PLS‐DA, forming a new algorithm named PSO‐SVWL‐PLSDA. Combined with ¹H nuclear magnetic resonance‐based metabonomics, PSO‐SVWL‐PLSDA was applied to recognize the patients with lung cancer from the healthy controls. PLS‐DA was also investigated as a comparison. Relatively to the recognition rates of 86% and 65%, which were yielded by PLS‐DA, respectively, for the training and test sets, those of 98.3% and 90% were offered by PSO‐SVWL‐PLSDA. Moreover, several most discriminative metabolites were identified by PSO‐SVWL‐PLSDA to aid the diagnosis of lung cancer, including lactate, glucose (α‐glucose and β‐glucose), threonine, valine, taurine, trimethylamine, glutamine, glycoprotein, proline, and lipid. Copyright © 2015 John Wiley & Sons, Ltd.     

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.