代谢组学研究技术及应用 生物系 赵闯营

目 录 什么是代谢组学 ? 代谢组学主要研究技术 代谢组学应用举例

系 统 生 物 学 Gene一 mRNA— Protein— >> Metabolite 系统生物学是后基因组研究最具挑战 性的一个研究领域。它包括转录组、蛋白质组、 代谢组学分析等分子生物学研究，数学分析，计 算机应用，模型建立和仿真等诸多方面的研究内 容 Gene一 mRNA— Protein— >> Metabolite

一、什么是代谢组学 1.代谢组学的提出 Jeremy K. Nicholson教授作为代谢组学的先行者，被誉为国际“代谢组学之父”. Metabonomics:the  quantitative  measurement  of  the   multi-parametric  metabolic response of  living  systems   to pathophysiological  stimuli  or  genetic  modifications. 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. 2.代谢组学的概念 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. 

Metabonomics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. 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  quantitative  measurement  of  all  low  molecular  weight   metabolites  in  an  organism cells  at  a  specified  time  under  specific  environmental  conditions).  Metabolomics 着重研究的是单个细 胞或细胞类型中所有的小分子成分和波动规律,或许可以叫做细胞代谢组学. 多用于植物和微生物系统. Metabonomics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  the  metabolite  complement  of  integrated  living  systems   and  its  dynamic  responses  to  the  changes  of  both  endogenous  factors  (such  as  physiology  and  development)  and  exogenous  factors  (such  as  environmental  factors  and  xenobiotics)). 因此，Metabolomics可以译作代谢物组学.Metabolomics 是Metabonomics的一个组成部分.  1应用于植物生理和微生物领域的研究 ,但也有应用于动物组织和细胞的，以GC/MS 为主, 2主要利用核磁共振技术和模式识别方法对生物体液和组织进行系 统测量和分析,对完整的生物体(而不是单个细胞) 中随时间改变的代谢物进行动态跟踪 检测、定量和分类,然后将这些代谢信息与病理生理过程中的生物学事件关联起来,从而 确定发生这些变化的靶器官和作用位点,进而确定相关的生物标志物

3.代谢组学的发展 1982 Van De Greef: publication of MS for urine profiling 1983 1984 1989 1999 2000 2001 2002 2007 Van De Greef: publication of MS for urine profiling Sadler, Buckingham and Nicholson: First publication on 1H-NMR of blood and plasma Nicholson, et al.: Multi-component analysis of spectra data from rat urine Nicholson and Wilson: NMR spectroscopy of biofluids Nicholson: Definition of Metabonomics Haselden, et al.: First independent Pharma publication of Metabonomics Nicholson, Lindon, and Holmes: Publication in Nature on Metabonomics Holmes and Antti Explanation of statistics in Metabonomics Increasing # of publications This slide outlines the history of metabonomics

二、代谢组学主要研究技术

代谢组学各分析流程技术

三、代谢组学应用举例

Abstract Proton-NMR-based metabonomics offers a rare opportunity as a definitive screening technique for biofluids and tissue biopsies. The procedure is extraordinary in that it allows the ‘complete biochemical picture’ to be examined at one time and is able to detect subtle but repeatedly consistent disparities that may be occurring in different, and perhaps unrelated, biochemical pathways. Such metabolic responses to an initial perturbation in homeostasis may be followed over a sequential time-course to their eventual dissipation or consequent sequelae. The application of this technique is beginning slowly to filter into the area of endocrine research and has been used to examine long-term and diffuse physiological alterations that may occur following such events as anabolic steroid treatment of cattle and the exposure of endometrial cells to tamoxifen. Although only modest inroads have been made so far, this technique promises immense potential for future researches within the endocrine field.

Contents 1. Introduction 2. Proton nuclear magnetic resonance spectroscopy 3. The metabonomic approach and pattern recognition 4. Application within the biological field 5. Hormones, drugs and the endocrine system References

1. What is Metabonomics? Metabonomics:the  quantitative  measurement  of  the   multi-parametric  metabolic response of  living  systems  to   pathophysiological  stimuli  or  genetic  modifications. (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) 2.　 Principle of proton-NMR 在特殊的磁场中，用无线电射频脉冲激发氢原子核，引起氢原子核共振，并吸收能量。在停止射频脉冲后，氢原子核按特定频率发出射电信号，并将吸收的能量释放出来，被体外的接受器收录，经电子计算机处理获得图像． 3. 1H-NMR and methods of analysis NMR data NLM HCA and PCA ……. unsupervised SIMCA NNs and PNNs ………supervised non-linear mapping(NLM), hierarchical cluster analysis (HCA) principal component analysis (PCA) soft independent modelling of class analogy (SIMCA) neural networks (NNs) probabilistic neural networks (PNNs)

4. Metabonomic approach in medical care 5. The immense potential of the metabonomic approach in the field of intermediary metabolism and endocrine control

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