生物芯片技术在微生物学中的应用研究进展

生物芯片技术是20世纪后期发展起来的新技术,在生命科学的各个领域逐渐得到广泛应用。本文就该技术在微生物学领域,特别是在病原微生物检验、病原微生物的基因组和基因变异性及基因多态性分析、病原微生物的致病机制、病原微生物感染后宿主机体基因表达的变化等方面的应用进行总结。     

饮食通过表观遗传作用于基因是影响健康的重要途径

机体衰老,肿瘤发生是基因表达变化的结果。饮食是影响健康的重要因素,与衰老、肿瘤关系密切。饮食可影响基因,但难用突变解释。近年表观遗传学研究逐渐深入,人们认识到饮食可通过DNA甲基化和组蛋白甲基化,这两种重要的表观遗传(epigenesis)方式：调控基因表达,进而对衰老、肿瘤等疾病的发生产生重要影响。     

创伤后胃肠外营养的理论基础

创伤(包括手术)后机体代谢变化的特点,是蛋白质代谢明显增强。分解的蛋白质的主要来源是骨髂肌。即使在创伤后的初期,机体的合成代谢也在照常进行。只要摄入适当的营养物质,机体就能使肝脏、胰脏这一类代谢活跃器官的重量保持稳定。创伤后营养的目的就是为了降低分解代谢,促进合成代谢,维持内环境的稳定,进而保护代谢活跃器官和生命重要器官,实现创伤愈合。近年来,对创伤后营养进行了广泛深入的研究。作为热原,主要依靠输入醣和脂肪。作为氮原,普遍主张使用适当配方的氨基酸溶液。另外,维生素、无机盐、以及微量元素也是营养液中必不可少的成分。     

基于基因互作网络熵量化细胞分化状态

细胞动态过程的研究表明,细胞在动态过程中会发生状态变化,主要由细胞内部的基因表达情况控制.随着高通量测序技术的发展,大量的基因表达数据能够在单细胞水平上获得细胞真实的基因表达信息.然而,现有大多数研究方法需要使用除基因表达以外其他的信息,带来了额外的复杂度和不确定性.此外,普遍存在的"缺失值"事件更是影响了对细胞动态发...     

Tup1突变对转录抑制局部网络中基因表达的非线性影响

研究基因网络的非线性行为特征是研制基因网络技术的基础。Tup1基因是酵母中作用最为广泛的转录抑制因子之一,利用酵母生物信息学数据库中蛋白相互作用关系,构建一个以Tup1为中心,4个层次741个基因的局部基因网络。统计分析六张Tup1不同突变的基因表达芯片数据,将局部基因网络中的全部基因按照3个网络特征进行统计分析：必需、非必需基因、网络层次和网络节点,并研究这些特征与基因表达之间的关系。初步发现基因表达变化的强度与节点数目成一定的反比关系,必需基因的平均变化程度较非必需基因为低,且由Tup1突变引发的其他基因的表达变化在以Tup1为中心的局部基因网络中近层次网络变化程度较大,远层次网络变化程度较低。     

光控基因表达系统

基因表达是生命的最本质特征之一,基因表达的调控对于研究生物体的各种生命现象具有至关重要的意义。长期以来,科学家们一直在追求可以在时间、空间上调控基因表达的技术。光遗传学的出现与快速发展已经允许人们以前所未有的时空精度调控基因表达。该文将介绍光控基因表达系统的研究进展,以及它们在疾病治疗、代谢工程以及合成生物学领域的应用;同时,探讨光控基因表达系统未来在各种应用中的意义和挑战。     

基因表达聚类分析技术的现状与发展

随着多个生物基因组测序的完成、DNA芯片技术的广泛应用,基因表达数据分析已成为后基因组时代的研究热点.聚类分析能将功能相关的基因按表达谱的相似程度归纳成类,有助于对未知功能的基因进行研究,是目前基因表达分析研究的主要计算技术之一.已有多种聚类分析算法用于基因表达数据分析,各种算法因其着眼点、原理等方面的差异,而各有其优缺点.如何对各种聚类算法的有效性进行分析、并开发新型的、适合于基因表达数据分析的方法已是当务之急.     

干旱、盐、温度对植物体NADP-苹果酸酶的影响与机理

NADP-苹果酸酶是植物体代谢的重要酶之一,参与了多个代谢过程,在植物体内广泛存在,与各种环境胁迫关系密切。目前,胁迫条件下的植物体NADP-苹果酸酶基因的表达情况以及酶活性的变化是关注的重点,同时,NADP-苹果酸酶在抗胁迫方面的机理研究也在逐渐的展开。综述了干旱、盐、高温和低温胁迫条件下NADP-苹果酸酶活性及该酶基因表达变化的特点,揭示了其在对植物体抵御各种胁迫带来的危害时所发挥的作用以及作用机理。     

DNA芯片技术研究进展及其在分子营养上的应用

DNA芯片技术是近年发展起来的又一新分子生物学研究工具,可使研究者得以自动化、快速、平行地对大量的生物信息加以分析,在基因组水平上研究基因表达。这种技术为从基因组水平研究基因表达水平与生理反应及生理状况的改变之间的关系提供了强有力的手段。通过比较不同营养水平或不同环境条件下的组织细胞基因达到表达谱差异,可以从基因组水平阐明各种营养成分或环境因素对动物机体的基因表达的影响,从而进一步揭示营养生理的机制和环境对动物影响的机理。DNA芯片技术为分子营养的研究开辟了一条崭新的道路,在从DNA芯片的原理、种类、实验设计、统计方法及在分子营养上的应用作一综述。     

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。     

提高外源基因在巴斯德毕赤酵母中表达量的研究进展

巴斯德毕赤酵母 (Pichiapastoris)表达系统是基因工程研究中广泛使用的真核表达系统 ,与现有的其它表达系统相比 ,巴斯德毕赤酵母在表达产物的糖基化修饰、折叠、加工、外分泌及表达量等方面有明显的优势。外源基因在该系统中表达时 ,由于受基因内部的结构、分泌信号、甲醇诱导的浓度及诱导时间、培养温度、启动子、表达环境的 pH值等诸多因素的影响 ,一些外源蛋白的表达也存在着表达不够稳定、表达量较低 ,甚至不表达的情况。对影响巴斯德毕赤酵母表达的各种可能因素进行了分析 ,结合具体实践经验 ,就如何提高外源基因在巴斯德毕赤酵母中表达量的问题进行了综述。     

The clustering of regression models method with applications in gene expression data

Identification of differentially expressed genes and clustering of genes are two important and complementary objectives addressed with gene expression data. For the differential expression question, many "per-gene" analytic methods have been proposed. These methods can generally be characterized as using a regression function to independently model the observations for each gene; various adjustments for multiplicity are then used to interpret the statistical significance of these per-gene regression models over the collection of genes analyzed. Motivated by this common structure of per-gene models, we proposed a new model-based clustering method--the clustering of regression models method, which groups genes that share a similar relationship to the covariate(s). This method provides a unified approach for a family of clustering procedures and can be applied for data collected with various experimental designs. In addition, when combined with per-gene methods for assessing differential expression that employ the same regression modeling structure, an integrated framework for the analysis of microarray data is obtained. The proposed methodology was applied to two microarray data sets, one from a breast cancer study and the other from a yeast cell cycle study.     

Global gene expression during the human organogenesis: from transcription profiles to function predictions

Human embryogenesis includes an integrated set of complex yet coordinated development of different organs and tissues, which is regulated by the spatiotemporal expression of many genes. Deciphering the gene regulation profile is essential for understanding the molecular basis of human embryo development. While molecular and genetic studies in mouse have served as a valuable tool to understand mammalian development, significant differences exists in human and mouse development at morphological and genomic levels. Thus it is important to carry out research directly on human embryonic development. Here we will review some recent studies on gene regulation during human embryogenesis with particular focus on the period of organogenesis, which had not been well studied previously. We will highlight a gene expression database of human embryos from the 4(th) to the 9(th) week. The analysis of gene regulation during this period reveals that genes functioning in a given developmental process tend to be coordinately regulated during human embryogenesis. This feature allows us to use this database to identify new genes important for a particular developmental process/pathway and deduce the potential function of a novel gene during organogenesis. Such a gene expression atlas should serve as an important resource for molecular study of human development and pathogenesis.     

Global analysis of gene expression in the estrogen induced pituitary tumor of the F344 rat

The F344 rat rapidly forms large prolactinomas in response to chronic estrogen treatment. To identify genes expressed in the course of this estrogen induced pituitary tumor growth, we performed microarray analysis on the F344 rat pituitary after chronic estrogen treatment and on untreated controls. At a significance level set to minimize type I error, some 72 genes were found to be differentially expressed between estrogen treated and untreated. Of those genes, 70 have not been reported previously as being affected by estrogen in the F344 rat pituitary. Since many other investigators have studied the effect of estrogen on specific gene expression in rat pituitary, we also examined the mRNA expression of the 36 genes that have been previously reported as having their expression affected by estrogen in the rat pituitary. Of these, 13 were found to have their expression affected by estrogen treatment in the same direction as had been reported by others.     

Gene expression profiling of human diseases by serial analysis of gene expression

Until recently, the approach to understanding the molecular basis of complex syndromes such as cancer, coronary artery disease, and diabetes was to study the behavior of individual genes. However, it is generally recognized that expression of a number of genes is coordinated both spatially and temporally and that this coordination changes during the development and progression of diseases. Newly developed functional genomic approaches, such as serial analysis of gene expression (SAGE) and DNA microarrays have enabled researchers to determine the expression pattern of thousands of genes simultaneously. One attractive feature of SAGE compared to microarrays is its ability to quantify gene expression without prior sequence information or information about genes that are thought to be expressed. SAGE has been successfully applied to the gene expression profiling of a number of human diseases. In this review, we will first discuss SAGE technique and contrast it to microarray. We will then highlight new biological insights that have emerged from its application to the study of human diseases.     

Dynamic models of gene expression and classification

Powerful new methods, like expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole genome expression. In this approach, a self-consistent calculation is performed that involves both linear and non-linear response terms for interrelating gene expression levels. This calculation uses singular value decomposition (SVD) not as a statistical tool but as a means of inverting noisy and near-singular matrices. The linear transition matrix that is determined from this calculation can be used to calculate the underlying network reflected in the data. This suggests a direct method of classifying genes according to their place in the resulting network. In addition to providing a means to model such a large multivariate system this approach can be used to reduce the dimensionality of the problem in a rational and consistent way, and suppress the strong noise amplification effects often encountered with expression profile data. Non-linear and higher-order Markov behavior of the network are also determined in this self-consistent method. In data sets from yeast, we calculate the Markov matrix and the gene classes based on the linear-Markov network. These results compare favorably with previously used methods like cluster analysis. Our dynamic method appears to give a broad and general framework for data analysis and modeling of gene expression arrays. Electronic Publication