代谢组学技术及其在临床研究中的应用

在后基因组时代, 系统生物学研究成为人们关注的焦点。转录组学、蛋白质组学等功能基因组学研究方法可同时检测药物或其他因素影响下大量基因或蛋白质的表达变化情况, 但这些变化不能与生物学功能的变化建立直接联系。代谢组学方法则可为代谢物含量变化与生物表型变化建立直接相关性。代谢组学研究的目的是定量分析一个生物系统内所有代谢物的含量, 进行全面代谢物分析需要分析化学技术的支撑, 核磁共振和基于质谱的分析技术是代谢组学研究的两种主要技术手段。代谢组学研究可产生大量数据信息, 对这些数据进行分析离不开化学统计学的应用, 比如主成分分析、多维缩放、各种聚类分析技术以及功能差异分析等。文章综述了近年来代谢组学分析技术及数据分析技术的研究进展, 在此基础上, 对代谢组学在临床研究及临床前研究中的应用研究进展进行了综述。对疾病代谢表型图谱的研究有助于人们了解疾病发生、发展以及致死的机制; 在临床条件下, 这些代谢图谱可以作为疾病诊断、预后以及治疗的评判标准。代谢物组成的变化是毒物胁迫对机体造成的最终影响, 利用代谢组技术可以直接反映毒物对机体的影响。质谱技术、核磁共振技术的应用使得药物筛选过程可以快速完成, 并有助于实现个性化用药。此外, 利用代谢组学技术还可以进行已知酶的新活性研究, 也可以研究未知酶。     

代谢组学：一个迅速发展的新兴学科

对代谢组学的含义,中心任务,研究方法,样品要求,应用及其发展方向进行了简要综述. 系统生物学概念的诞生标志着研究哲学由“还原论”向“整体论”的变化. 系统生物学的中心任务就是要针对生物系统整体 (无论它是生物细胞,多细胞组织,器官还是生物整体),建立定量,普适,整体和可预测 (QUIP) 的认知. 具体而言,系统生物学研究就是要将给定生物系统的基因,转录,蛋白质和代谢水平所发生的事件,相关性及其对所涉及生物过程的意义进行整体性认识. 从而出现了许多的“组”和“组学”的新概念. 但是现已提出的一百多个“组”和“组学”,可以大体归纳为“基因组”/“基因组学”,“转录组”/“转录组学”,“蛋白质组”/“蛋白质组学”和“代谢组”/“代谢组学”四个方面. 显而易见,DNA,mRNA 以及蛋白质的存在为生物过程的发生提供了物质基础 (但这个过程有可能不发生!),而代谢物质所反映的是已经发生了的生物学事件. 因此代谢组学是对一个生物系统进行全面认识的不可缺少的一部分,是全局系统生物学 (global systems biology) 的重要基础,也是系统生物学的一个重要组成部分. 在现有的英文表述中,代谢组学同时存在两个不同的词汇和概念,即metabonomics 和 metabolomics. 尽管前者多用在动物系统而后者多用于植物和微生物系统,但这些概念的本质从他们的定义中能够得到较细致的了解. Metabonomics 的最初定义是就生物系统对生理和病理刺激以及基因改变的代谢应答的定量测定(“the quantitative measurement of the multi-parametric metabolic response of living systems to pathophysiological stimuli or geneticmodifications”). 我们认为这个定义现在可以更广泛地表述为:代谢组学是关于定量描述生物内源性代谢物质的整体及其对内因和外因变化应答规律的科学 (“Metabonomics is the branch of science concerned with the quantitative understandings of themetabolite complement of integrated living systems and its dynamic responses to the changes of both endogenous factors (such asphysiology and development) and exogenous factors (such as environmental factors and xenobiotics).”). 其中心任务包括 (1) 对内源性代谢物质的整体及其动态变化规律进行检测,量化和编录,(2) 确定此变化规律和生物过程的有机联系. Metabolomics 存在多个定义,但其精髓是:对一个生物系统的细胞在给定时间和给定条件下所有小分子代谢物质的定量分析(the quantitativemeasurement of all low molecular weight metabolites in an organism's cells at a specified time under specific environmentalconditions). 因此,metabolomics 可以译作“代谢物组学”. 不难看出,前者是对生物系统进行的整体和动态的认识 (不仅关心代谢物质的整体也关注其动态变化规律),而后者强调分析而且是个静态的认识概念. 因此可以认为,metabolomics 是metabonomics 的一个组成部分 (参看定义). 近年又有人提出了“dynamic metabolomics”的概念,这个概念所表达的含义十分接近“metabonomics”本身的含义. 所以,可以预见,随着这门新兴学科的发展和更深入讨论,这两个概念必将趋向一致. 因此我们建议,在中文表述中将“代谢组学”一词和英文中的 metabonomics 相对应,以避免不必要的混淆和争议. 就细胞系统而言,不仅存在细胞自身的代谢物质组成问题,存在细胞之间代谢物质交换的问题,也存在代谢过程所发生的位点问题. 因此,简单地分析代谢物质的总组成 (即代谢组) 缺乏“整体论”所要求的全面性,其意义有一定局限. 代谢组学属于全局系统生物学 (Global systems biology) 研究方法,便于对复杂体系的整体进行认识. 譬如,一个正常工作的人体包括“人体”本身和与之共同进化而来且共生的消化道微生物群体 (或称菌群),孤立地研究“人体”本身的基因,转录子以及蛋白质当然可以为人们认识人体生物学提供重要信息,但无法提供使人体正常工作不可缺少的菌群的信息. 人体血液和尿液的代谢组却携带着包括菌群在内的每一个细胞的信息,因此代谢组学方法对研究如人体这样复杂的进化杂合体十分有效. 正因如此,代谢组学已经广泛地应用到了包括药物研发,分子生理学,分子病理学,基因功能组学,营养学,环境科学等重要领域. 在代谢组学诞生的过去 6 年里,有关代谢组学的研究论文和专利以指数的形式逐年增长. 可以预见,这门新兴学科将应用到更为广泛的领域.     

代谢组学技术在水产动物疾病研究中的应用

代谢组学是定量描述生物内源性代谢物质的整体及其对内因和外因变化应答规律的的一门新学科。近年来,代谢组学技术在水产动物疾病中的研究备受关注,特别是为感染性疾病发生机制及防控研究提供了一种新的手段。本文介绍了代谢组学技术及其在水产动物研究中的应用,包括代谢组学技术在水产动物感染性疾病、细菌耐药及环境应激等方面应用进行综述,分析了代谢组学在水产动物疾病研究中面临的问题与挑战,并对未来水产动物代谢组学研究趋势进行了展望,以期为代谢组学技术在水产动物疾病发病机制和药物研发方面更深入的运用提供参考。     

Nutritional metabonomics: applications and perspectives

Nowadays, nutrition focuses on improving health of individuals through diet. Current nutritional research aims at health promotion, disease prevention, and performance improvement. Modern analytical platforms allow the simultaneous measurement of multiple metabolites providing new insights in the understanding of the functionalities of cells and whole organisms. Metabonomics, "the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modifications", provides a systems approach to understanding global metabolic regulations of organisms. This concept has arisen from various applications of NMR and MS spectroscopies to study the multicomponent metabolic composition of biological fluids, cells, and tissues. The generated metabolic profiles are processed by multivariate statistics to maximize the recovery of information to be correlated with well-determined stimuli such as dietary intervention or with any phenotypic data or diet habits. Metabonomics is thus uniquely suited to assess metabolic responses to deficiencies or excesses of nutrients and bioactive components. Furthermore, metabonomics is used to characterize the metabolic phenotype of individuals integrating genetic polymorphism, metabolic interactions with commensal and symbiotic partners such as gut microflora, as well as environmental and behavioral factors including dietary preferences. This paper reports several experimental key aspects in nutritional metabonomics, reviews its applications employing targeted and holistic approach analysis for the study of the metabolic responses following dietary interventions. It also reports the assessment of intra- and inter-individual variability in animal and human populations. The potentialities of nutritional metabonomics for the discovery of new biomarkers and the characterization of metabolic phenotypes are discussed in a context of their possible utilizations for personalized nutrition to provide health maintenance at the individual level.     

Metabonomics in pharmaceutical R&D

This minireview is based on a lecture given at the First Maga Circe Conference on metabolomics held at Sabaudia, Italy, in March 2006 in which the analytical and statistical techniques used in metabonomics, efforts at standardization and some of the major applications to pharmaceutical research and development are reviewed. Metabonomics involves the determination of multiple metabolites simultaneously in biofluids, tissues and tissue extracts. Applications to preclinical drug safety studies are illustrated by the Consortium for Metabonomic Toxicology, a collaboration involving several major pharmaceutical companies. This consortium was able, through the measurement of a dataset of NMR spectra of rodent urine and serum samples, to build a predictive expert system for liver and kidney toxicity. A secondary benefit was the elucidation of the endogenous biochemicals responsible for the classification. The use of metabonomics in disease diagnosis and therapy monitoring is discussed with an exemplification from coronary artery disease, and the concept of pharmaco-metabonomics as a way of predicting an individual's response to treatment is exemplified. Finally, some advantages and perceived difficulties of the metabonomics approach are summarized.     

LC-MS-based metabonomics analysis

Metabonomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. It has shown particular promise in the areas of toxicology and drug development, functional genomics, systems biology, and clinical diagnosis. Comprehensive metabonomics investigations are primarily a challenge for analytical chemistry. High-performance liquid chromatography-mass spectrometry (HPLC-MS) is an established technology in drug metabolite analysis and is now expanding into endogenous metabolite research. Its main advantages include wide dynamic range, reproducible quantitative analysis, and the ability to analyze biofluids with extreme molecular complexity. The aims of developing HPLC-MS for metabonomics range from understanding basic biochemistry to biomarker discovery and the structural characterization of physiologically important metabolites. In this review, the strategy and application of HPLC-MS-based metabonomics are reviewed.     

A novel R-package graphic user interface for the analysis of metabonomic profiles

Background  

Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries.     

代谢物组学在医药研究中的成功实践

代谢物组学作为后基因时代的一种全新的组学技术。其主要以现代系统生物学为理论基础,以生物体液为研究对象,以现代谱学分析理论和生物样品制备方法为技术支撑,集中生物体内低分子量化学组分进行全息分析和海量数据挖掘,最终明晰机体生物学变化的本质。代谢物组学在功能基因组学、病理生理学、药理毒理学等方面都有着广泛的应用前景。本文以代谢物组学概念化的提出为切入点,着眼于代谢物组学的宽口径应用领域,重点概述代谢物组学在医药领域的成功实践,并对代谢物组学的未来发展做初步构想代谢物组学在功能基因组学、病理生理学、药理毒理学等方面都有着广泛的应用前景。     

代谢组学技术及其在毒理学研究领域中的应用

代谢组学是近几年发展起来的对某一生物或细胞所有低分子量代谢产物进行定性和定量分析的一门新学科,其研究对象主要是生物体液,研究手段主要是核磁共振和质谱。简要综述了代谢组学的概念、代谢组学在毒理学研究领域中的应用、当前代谢组学研究中存在的问题及今后的发展趋势,并探讨了代谢组学在研究毒物作用机制、药物的临床前安全性评价、确定毒物作用靶器官及器官特异性新的生物标志物中的实际应用。     

代谢组学在临床研究中的应用及进展

代谢组学是"后基因组学"时期新兴的一门学科,也是系统生物学的重要组成部分。代谢组学通过全面、定量检测生物样本中多种类型小分子化合物,来了解在内在和外界因素作用下生物体内源性物质的变化及规律,特别适合于临床上研究机体因受到遗传、生长、生理、环境因素和异物、病源等刺激的影响而产生的变化。借助于代谢组学技术不仅能够描述疾病发生、发展以及治疗过程中机体代谢机能的状态和变化,为临床疾病的诊断、病理机制的探索、新治疗靶点的发现等提供新的途径和思路,还可以揭示外界干扰因素(药物/毒物、环境、饮食、生活方式等)对机体的影响,为药效评价和疾病病因的筛查提供基础数据。近年来,代谢组学在临床研究方面得到了广泛的应用,取得了巨大的进展并展现了鼓舞人心的应用前景。该文分别就代谢组学在描述疾病发展状态、研究疾病诊断方法、探索疾病发病原因和发病机理、药效学评价等几个方面的应用及进展进行回顾和综述。     

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma

In this work, the variations in the metabolic profile of blood plasma from lung cancer patients and healthy controls were investigated through NMR-based metabonomics, to assess the potential of this approach for lung cancer screening and diagnosis. PLS-DA modeling of CPMG spectra from plasma, subjected to Monte Carlo Cross Validation, allowed cancer patients to be discriminated from controls with sensitivity and specificity levels of about 90%. Relatively lower HDL and higher VLDL + LDL in the patients' plasma, together with increased lactate and pyruvate and decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), and methanol, could be detected. These changes were found to be present at initial disease stages and could be related to known cancer biochemical hallmarks, such as enhanced glycolysis, glutaminolysis, and gluconeogenesis, together with suppressed Krebs cycle and reduced lipid catabolism, thus supporting the hypothesis of a systemic metabolic signature for lung cancer. Despite the possible confounding influence of age, smoking habits, and other uncontrolled factors, these results indicate that NMR-based metabonomics of blood plasma can be useful as a screening tool to identify suspicious cases for subsequent, more specific radiological tests, thus contributing to improved disease management.     

Tumour banks: a major stake for both patient care and research

Tumour banks are created by pathology and haematology departments, using cell and tissue samples from patients, obtained for diagnostic purpose, and secondarily requalified for research. These banks are the most important source of human samples for research. It appears important to maintain research programs on human tissues and cells, since cell lines and animal models only partly reflect human lesions. Tumours banks are also important for patient care, since they allow patients to benefit from recent therapeutic advances. Indeed some drugs only kill some tumour cells, which can be identified from the initial tumour samples by means of specific markers. Moreover, tumours banks are the choice tool for transferring scientific discoveries to daily patients care in oncology.     

Automated SPE-RP-HPLC fractionation of biofluids combined to off-line NMR spectroscopy for biomarker identification in metabonomics

NMR-based metabonomics is a valuable and straightforward approach to measuring hundreds of metabolites in complex biofluids. However, metabolite identification is sometimes limited by overlapped signals in NMR spectra. We describe a new methodology using an automated hyphenation of solid phase extraction (SPE) with RP-HPLC combined to NMR spectroscopy, which allowed identification of 72 metabolites of various molecular classes in human urine. This methodology was also successfully applied to the fractionation of a cat urine sample to aid identification of aromatic compounds and felinine. The SPE-RP-HPLC method appears to be a reliable tool to support biomarker discovery in metabonomic studies.     

气相色谱-质谱联用技术及其在代谢组学中的应用

代谢组学是以高通量、高灵敏度、高分辨率的现代仪器分析方法为手段,对细胞、体液、组织中所有代谢物进行无偏向的定性与定量分析的一门学科。气相色谱-质谱联用技术具有较高的检测灵敏度和鉴定准确度,通过标准谱图库的比对可对代谢物进行快速的鉴定,因此被广泛应用于生物样品的代谢产物的检测中。文中对近年来气相色谱-质谱联用技术的发展以及在代谢组学研究中取得的成果进行了综述。首先介绍了气相色谱-质谱联用技术的分类和常用的样品衍生化方法;继而从样品预处理、定性与定量分析、数据分析三方面介绍了气相色谱-质谱联用技术分析代谢物的方法,并系统地对该技术在微生物、植物、疾病诊断领域的应用实例进行了评述;最后提出了当前气相色谱-质谱联用技术在代谢组学研究中存在的问题并对后续的研究进行了展望。     

Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles

Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields, including work in cardiovascular research and drug toxicology. In this study, metabonomics method was employed to the diagnosis of Type 2 diabetes mellitus (DM2) based on serum lipid metabolites. The results suggested that serum fatty acid profiles determined by capillary gas chromatography combined with pattern recognition analysis of the data might provide an effective approach to the discrimination of Type 2 diabetic patients from healthy controls. And the applications of pattern recognition methods have improved the sensitivity and specificity greatly.     

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine

In this study, 1H NMR-based metabonomics has been applied, for the first time to our knowledge, to investigate lung cancer metabolic signatures in urine, aiming at assessing the diagnostic potential of this approach and gaining novel insights into lung cancer metabolism and systemic effects. Urine samples from lung cancer patients (n = 71) and a control healthy group (n = 54) were analyzed by high resolution 1H NMR (500 MHz), and their spectral profiles subjected to multivariate statistics, namely, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Projections to Latent Structures (OPLS)-DA. Very good discrimination between cancer and control groups was achieved by multivariate modeling of urinary profiles. By Monte Carlo Cross Validation, the classification model showed 93% sensitivity, 94% specificity and an overall classification rate of 93.5%. The possible confounding influence of other factors, namely, gender and age, have also been modeled and found to have much lower predictive power than the presence of the disease. Moreover, smoking habits were found not to have a dominating influence over class discrimination. The main metabolites contributing to this discrimination, as highlighted by multivariate analysis and confirmed by spectral integration, were hippurate and trigonelline (reduced in patients), and β-hydroxyisovalerate, α-hydroxyisobutyrate, N-acetylglutamine, and creatinine (elevated in patients relatively to controls). These results show the valuable potential of NMR-based metabonomics for finding putative biomarkers of lung cancer in urine, collected in a minimally invasive way, which may have important diagnostic impact, provided that these metabolites are found to be specifically disease-related.     

Diagnosis of liver cancer using HPLC-based metabonomics avoiding false-positive result from hepatitis and hepatocirrhosis diseases

Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields. Current metabonomics practice has relied on mass spectrometry (MS), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) to analyze metabolites. In this study, a novel approach of using high-performance liquid chromatography (HPLC) in conjunction with developed software was employed. Using the principal components analysis method (PCA), all (113) peaks of urinary metabolites with a cis-diol structure from patients with hepatitis and hepatocirrhosis were compared to those from liver cancer patients. The results showed that the metabonomics-PCA method might be useful to differentiate between patients with hepatocirrhosis and hepatitis from patients with liver cancer while lowering false-positive rate. These findings also suggest that a subset of the urinary nucleosides identified with metabonomics correlate better with cancer diagnosis than the traditional single tumor marker alpha-fetoprotein (AFP).