代谢组研究

代谢是生命活动中所有（生物）化学变化的总称。代谢活动是生命活动的本质特征和物质基础。代谢组是生物体内源性代谢物质的动态整体。代谢组学是关于生物体内源性代谢物质的整体及其变化规律的科学。系统生物学研究的本质就是要求对研究对象的相关分子机理进行定量、普适、整体和可预测性地认识。作为全局系统生物学的基础和系统生物学的一个重要组成部分,代谢组学是以物理学基本原理为基础的分析化学、以数学计算与建模为基础的化学计量学和以生物化学为基础的生命科学等学科交叉的学科。在过去七年多的时间里,这门新兴的学科得到了迅速的发展,并已广泛地应用到了分子病理学、毒理学、功能基因组学、临床医学和环境科学等领域。本文就代谢组学的本质、代谢组分析研究方法及其应用做了概述。     

代谢组学在乳腺癌生物标志物研究中的现状及展望

代谢组学是近年来系统生物学研究领域中最活跃的分支学科之一,通过研究生命体在不同生理病理状态下所产生的内源性代谢物变化规律来揭示疾病相关的代谢本质.作为系统生物学研究中的后起之秀,代谢组学以其特独的研究视角和技术优势迅速而广泛地应用于癌症研究领域,已初步展现出广阔的临床应用前景.乳腺癌是女性最常见的恶性肿瘤,严重威胁着女性健康且呈年轻化趋势.利用代谢组学技术平台对乳腺癌进行研究,寻找潜在的代谢性生物标志物,有助于乳腺癌的早期诊断、疗效评价、预后监控及制定合适的个体化治疗方案,对改善乳腺癌患者的存活率具有重要意义.本文将对代谢组学的概念、研究方法及其在乳腺癌生物标志物研究中的应用进行综述.     

代谢组学研究进展及其应用

代谢组学是系统生物学的重要组成部分,其通过研究生物体代谢物的变化来认识生命体的生理与生化状态,从而找出其中隐藏的规律。对代谢组学的含义,研究任务进行介绍;综述代谢组学的产生和技术平台及其在植物、微生物、疾病诊断及毒物学等领域的应用,并对代谢组学的发展趋势以及面临的挑战等问题进行评述。     

细胞代谢组学样品前处理研究进展

细胞代谢组学是系统生物学的重要组成部分,是对生物系统进行整体和动态的认识的科学,主要进行小分子代谢物定性和定量分析研究,观察代谢物的浓度变化,从而在细胞水平上考察代谢机制。细胞代谢组学的工作流程包括:实验设计、样品采集、样品处理、代谢物分析和数据处理。其中,样品前处理方法不尽相同,而设计一个合理方便的样品前处理方法对后期开展代谢组学至关重要。现简要综述现阶段对细胞代谢组学样品前处理的研究成果和常用方法。     

基于GC-MS的植物代谢组学研究

代谢组学是系统生物学的重要组成部分。在众多代谢组学分析技术中,气相色谱-质谱联用(GC-MS)技术较为成熟,分辨率高、灵敏度高、重现性好,拥有大量标准质谱图数据库且成本相对低廉,很早就应用到植物代谢组学研究领域,迄今仍然是主要分析平台之一。现从GC-MS技术的核心原理、衍生化方法以及数据采集、分析和数据库等方面介绍了GC-MS植物代谢组学分析技术。同时,综述了该技术在基因功能研究、植物代谢遗传机理、植物抗逆、生物技术和生物育种中的应用。     

代谢组学在乳酸菌研究中的应用

代谢组学是系统生物学的重要分支,因其高效、高通量等特点而广泛应用于食品科学、药物学等研究领域。本文概述了代谢组学的分离和检测技术,综述了代谢组学在乳酸菌鉴定、发酵调控、肠道菌群研究等方面中的应用,对代谢组学在乳酸菌研究中潜在的问题和未来发展趋势进行了讨论,期望为代谢组学在食品工业微生物中的应用提供参考。     

生态代谢组学研究进展

代谢组学指某一生物系统中产生的或已存在的代谢物组的研究,以质谱和核磁共振技术为分析平台,以信息建模与系统整合为目标。随着代谢组学中的研究方法与技术成为生态学研究的有力工具,生态代谢组学概念应运而生,即研究某一个生物体对环境变化的代谢物组水平的响应。理清代谢组学与生态代谢组学学科发展的脉络,综述代谢组学研究中的常用技术及其优势与局限性,论述代谢组学技术在生态学研究中的应用现状,展望代谢组学技术与其他系统生物学组学技术的结合在生态学中的应用前景,提出生态代谢组学研究者未来要完成的任务和面对的挑战。     

基因组学、蛋白质组学和代谢组学在微生物降解PAHs中的应用

基因组学、蛋白质组学和代谢组学是系统生物学的重要组成部分,是近年来发展出来的新兴学科。随着生命科学的研究进入了多组学时代,基因组学、蛋白质组学和代谢组学得到了迅猛发展,被广泛应用在环境微生物学的各个研究领域,并成为研究PAHs微生物降解中不可或缺的重要手段。主要阐述了3大组学在微生物降解PAHs内在机理及代谢通路中的最新研究进展,并展望了3大组学在多环芳烃微生物降解机制中的应用前景与挑战。     

代谢组学在疾病诊断中的应用

代谢组学（metabolomics）的出现是生命科学研究的必然。在20世纪90年代中期发展起来的代谢组学,是对某一生物或细胞中相对分子量小于1,000的小分子代谢产物进行定性和定量分析的一门新学科。代谢组作为系统生物学的重要组成部分,在临床医学领域具有广泛的应用前景。     

代谢组学在中药研究中的应用进展

代谢组学是功能基因组学和系统生物学研究不可或缺的重要组成部分,是通过考察生物体系受刺激或扰动前后代谢产物的动态变化,研究生物体系的代谢网络的一种技术。应用代谢组学高通量、整体性的研究思路来理解中药的作用过程,与中医药的整体、辩证观点是一致的。代谢组学已成为中药研发的一个重要途径和手段,为中药现代化在技术上提供巨大支持,有助于为中药现代化研究寻找更多有效的突破口。本文在前人综述的基础上,着重概括了中药代谢组学研究方法近3年来在中药有效物质基础和作用机制、药物作用模型的鉴别和确证、毒性研究和中药安全性评价等方面的应用情况,同时展望了代谢组学方法所面临的机遇和挑战。     

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.     

Artificial neural networks and their use in quantitative pathology

A brief general introduction to artificial neural networks is presented, examining in detail the structure and operation of a prototype net developed for the solution of a simple pattern recognition problem in quantitative pathology. The process by which a neural network learns through example and gradually embodies its knowledge as a distributed representation is discussed, using this example. The application of neurocomputer technology to problems in quantitative pathology is explored, using real-world and illustrative examples. Included are examples of the use of artificial neural networks for pattern recognition, database analysis and machine vision. In the context of these examples, characteristics of neural nets, such as their ability to tolerate ambiguous, noisy and spurious data and spontaneously generalize from known examples to handle unfamiliar cases, are examined. Finally, the strengths and deficiencies of a connectionist approach are compared to those of traditional symbolic expert system methodology. It is concluded that artificial neural networks, used in conjunction with other nonalgorithmic artificial intelligence techniques and traditional algorithmic processing, may provide useful software engineering tools for the development of systems in quantitative pathology.     

Classification of Camellia (Theaceae) species using leaf architecture variations and pattern recognition techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species.     

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。     

Dual detection of fungal infections in Drosophila via recognition of glucans and sensing of virulence factors

The Drosophila immune system discriminates between various types of infections and activates appropriate signal transduction pathways to combat the invading microorganisms. The Toll pathway is required for the host response against fungal and most Gram-positive bacterial infections. The sensing of Gram-positive bacteria is mediated by the pattern recognition receptors PGRP-SA and GNBP1 that cooperate to detect the presence of infections in the host. Here, we report that GNBP3 is a pattern recognition receptor that is required for the detection of fungal cell wall components. Strikingly, we find that there is a second, parallel pathway acting jointly with GNBP3. The Drosophila Persephone protease activates the Toll pathway when proteolytically matured by the secreted fungal virulence factor PR1. Thus, the detection of fungal infections in Drosophila relies both on the recognition of invariant microbial patterns and on monitoring the effects of virulence factors on the host.     

The Kautsky curve is a built-in barcode.

We identify objects from their visually observable morphological features. Automatic methods for identifying living objects are often needed in new technology, and these methods try to utilize shapes. When it comes to identifying plant species automatically, machine vision is difficult to implement because the shapes of different plants overlap and vary greatly because of different viewing angles in field conditions. In the present study we show that chlorophyll a fluorescence, emitted by plant leaves, carries information that can be used for the identification of plant species. Transient changes in fluorescence intensity when a light is turned on were parameterized and then subjected to a variety of pattern recognition procedures. A Self-Organizing Map constructed from the fluorescence signals was found to group the signals according to the phylogenetic origins of the plants. We then used three different methods of pattern recognition, of which the Bayesian Minimum Distance classifier is a parametric technique, whereas the Multilayer Perceptron neural network and k-Nearest Neighbor techniques are nonparametric. Of these techniques, the neural network turned out to be the most powerful one for identifying individual species or groups of species from their fluorescence transients. The excellent recognition accuracy, generally over 95%, allows us to speculate that the method can be further developed into an application in precision agriculture as a means of automatically identifying plant species in the field.     

基因组尺度分析和代谢工程策略

代谢工程是近年来发展起来的新技术,随着各种组学技术的发展,高通量数据整合方法用于分析细胞的代谢网络,改造代谢途径,以提高目标产物的产量。本文就代谢工程的发展状况,基因组尺度的分析技术,以及代谢工程策略进行了综述。分析了生物信息学和系统生物学方法在代谢途径构建和代谢网络分析中的作用,并就存在的问题和可能的解决途径进行了阐述。     

TLR7 Recognition Is Dispensable for Influenza Virus A Infection but Important for the Induction of Hemagglutinin-Specific Antibodies in Response to the 2009 Pandemic Split Vaccine in Mice

Recognition of pathogen-associated molecular patterns by pattern recognition receptors of the innate immune system is crucial for the initiation of innate and adaptive responses and for immunological memory. We investigated the role of TLR7 in the induction of adaptive immunity and long-term memory following influenza virus infection and vaccination in C57BL/6 mice. During infection with influenza A/PR8/34 virus, the absence of either TLR7 or MyD88 leads to reduced virus-specific antibodies in the serum and antibody-secreting cells in their secondary lymphoid organs, particularly in bone marrow. In spite of this, the absence of TLR7/MyD88 signaling did not impair the production of protective antibodies. Following immunization with the 2009 pandemic inactivated split vaccine, TLR7(-/-) mice had significantly lower levels of germinal center formation, antibody-secreting cells, and circulating influenza virus-specific antibodies than control animals. Consequently, TLR7(-/-) mice failed to develop protective immunological memory upon challenge. Furthermore, the immunogenicity of the split vaccine was likely due to TLR7 recognition of virion RNA, as its removal from the split vaccine significantly reduced the levels of influenza virus-specific antibodies and compromised the vaccine protective efficacy in mice. Taken together, our data demonstrate that TLR7 plays an important role in vaccine-induced humoral immune responses to influenza virus through the interaction with viral RNA present in the split vaccine.