全局转录调控及其在代谢工程中的应用

全局转录调控是一种全新的改进细胞表型的定向进化方法,通过error-prone PCR、DNA shuffling等技术对细胞中的σ因子和其他转录元件进行多轮突变修饰,改变RNA聚合酶的转录效率和对启动子的亲和能力,使细胞的转录在整体水平上发生改变,导致许多由多种基因控制的细胞表型得以改进。全局转录调控可以对代谢途径快速优化,在代谢工程中已被成功地应用于各种代谢产物的生物合成中。随着全局转录调控理论的不断完善,其应用前景也将越来越广阔。     

酿酒酵母中基于CRISPR/dCas9的基因转录调控工具的开发与应用

酿酒酵母(Saccharomyces cerevisiae)是重要的模式真核微生物,广泛用于基础研究和工业发酵。基于CRISPR/dCas9系统开发的转录调控方法具有可编程、多重性和正交性等优点,在酿酒酵母的基因调控、功能基因组学、代谢工程等研究领域具有巨大潜力。本文关注酿酒酵母中CRISPR/dCas9基因转录调控工具的研究进展,阐述了不同转录调节结构域对dCas9或gRNA活性的调节,设计与优化dCas9和gRNA表达的方法,影响CRISPR/dCas9系统转录调控效率、特异性和通量的靶向性因素,最后总结了该工具在酿酒酵母代谢工程中的应用,并对该技术的未来发展提出了展望。     

原核生物转录调控研究技术及进展

原核生物基因表达调控主要发生在转录水平,研究原核生物的转录调控有利于了解其基因表达调节机制。近年来,随着分子生物学及相关技术的突破,转录调控研究技术也不断发展,因此主要综述了原核生物转录调控的技术方法及其新进展,包括凝胶电泳迁移率实验、DNase I足迹技术、染色质免疫共沉淀技术、微量热泳动技术、等温滴定量热法及细菌单杂交系统,以期系统地了解这些方法的优缺点和适用性,帮助研究者更好的利用原核生物转录调控为人类造福。     

酿酒酵母转录调控位点生物信息学研究进展

随着转录调控网络成为后基因组时代研究的最重要的问题之一,研究转录因子结合位点对此有着重要意义,生物信息学在转录因子结合位点的研究中也发挥着越来越关键的作用。方法:本文对目前研究最早最成熟的真核模式生物酿酒酵母基因组转录调控位点生物信息学研究现状进行分析。结果:本文总结了常用的转录因子相关的数据库、转录因子结合位点的表示方法、预测算法,并简要阐述了调控网络的类型和研究现状。结论:本研究结果为深入研究真核生物的转录水平调控模式奠定理论基础。     

细菌胞外多糖生物合成转录调控因子研究进展

细菌胞外多糖(Exopolysaccharide,EPS)因其独特的理化特性和生理活性,在食品、制药和化工等领域广泛应用。在食品行业中,黄原胶、结冷胶和热凝胶等细菌EPS备受青睐。转录调控因子能在转录水平上调控eps基因的表达,影响细菌EPS的生物合成。目前细菌EPS转录调控因子的研究报道较少,且多数已知的EPS转录因子调控机制尚未阐明。本文总结了近年来细菌EPS调控因子的研究进展,重点介绍其研究方法和调控机制,以期为细菌EPS转录调控研究提供借鉴。     

植物转录因子最新研究方法

转录因子可以调控众多下游基因的表达,在植物的生长发育、代谢及对外界环境的反应中起着重要作用。我们结合近年来植物转录因子的研究进展,归纳分析了高等植物转录因子研究的主要策略和最新的技术方法,并从生物信息学分析、瞬间转化技术的应用、突变体表型分析及调控网络等几个方面进行了全面阐述,为植物转录因子的预测、功能鉴定及靶基因分析等相关研究提供理论和方法的参考。     

真核基因转录与转录调节相关复合物

真核细胞核中转录因子与染色质模板如何相互作用调节基因转录是基因表达调控研究的一个中心问题.近来的研究表明,参与基因转录的各种调节因子在核内形成多种复合物,如RNA聚合酶Ⅱ全酶、染色质重塑复合物、核小体以及增强小体等.这些复合物之间相互作用,调节染色质结构,在染色质模板上进一步组装成转录复合物,参与转录调节的各个环节,调节转录复合物活性.这些复合物的形成,整合了转录调节的各种信息,提高了转录调节效率,是真核基因有效、严格、有序表达的基础.另一方面,这些复合物的存在给基因表达调控的研究提出了新问题,发展新的研究思路和新的研究技术具有重要意义.     

枯草芽孢杆菌表达与调控工具相关研究进展

枯草芽孢杆菌作为革兰氏阳性模式微生物,由于其清晰的遗传背景、高效的分泌能力以及简单的培养条件等优势被广泛的应用于生物技术产业。近年来,随着代谢工程与合成生物学的发展,枯草芽孢杆菌相关表达系统与调控工具研究也取得了很大进展。围绕枯草芽孢杆菌动态调控工具的研究进展,分别从转录水平调控和转录后水平调控两个层面上进行综述,并对调控元件在生物技术中的应用进行了讨论。最后,对未来枯草芽孢杆菌表达与调控工具的发展进行了展望。     

转录因子与microRNA在基因表达调控中的功能联系及差异

转录因子和微RNA(microRNA)是最大的两类反式作用因子,它们是基因表达调控的重要调控因子.它们协调发挥调控作用,精细调控基因的表达,在细胞分化和动物生长发育过程中发挥重要的作用.随着对转录因子和microRNA研究的深入,人们发现转录因子和microRNA在基因表达调控网络中关系紧密,它们的分子作用机制有许多相似之处,两者都通过各自的顺式作用元件调控基因表达,且作用的方式类似.但转录因子和microRNA也存在不同之处,转录因子既可以激活基因表达,也可抑制基因表达,而microRNA主要是抑制基因表达.另外,转录因子调控区的复杂性一般高于microRNA的调控区域.本文综述了转录因子和microRNA的异同点,并提出了未来转录因子和microRNA的研究方向.     

植物microRNA及其抗胁迫作用机制研究进展

植物中广泛存在的microRNA是一类长度约20~24 nt的非编码RNA,它作为负调控因子,通过降解目的基因或抑制目的基因的翻译作用,在转录后水平调控基因的表达.植物microRNA参与生长发育等功能的调控,并在抗生物或非生物胁迫中发挥着重要的作用,如调控植物体内磷、硫的代谢平衡及应对氧化胁迫等生理过程.本文对植物microRNA的特点、形成、作用机制、功能及研究技术方法进行了综述.     

RESOURCERER: a database for annotating and linking microarray resources within and across species

Microarray expression analysis is providing unprecedented data on gene expression in humans and mammalian model systems. Although such studies provide a tremendous resource for understanding human disease states, one of the significant challenges is cross-referencing the data derived from different species, across diverse expression analysis platforms, in order to properly derive inferences regarding gene expression and disease state. To address this problem, we have developed RESOURCERER, a microarray-resource annotation and cross-reference database built using the analysis of expressed sequence tags (ESTs) and gene sequences provided by the TIGR Gene Index (TGI) and TIGR Orthologous Gene Alignment (TOGA) databases [now called Eukaryotic Gene Orthologs (EGO)].     

Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity

Gene expression signatures from microarray experiments promise to provide important prognostic tools for predicting disease outcome or response to treatment. A number of microarray studies in various cancers have reported such gene signatures. However, the overlap of gene signatures in the same disease has been limited so far, and some reported signatures have not been reproduced in other populations. Clearly, the methods used for verifying novel gene signatures need improvement. In this article, we describe an experiment in which microarrays and sample hybridization are designed according to the statistical principles of randomization, replication and blocking. Our results show that such designs provide unbiased estimation of differential expression levels as well as powerful tests for them.     

Multiscale Embedded Gene Co-expression Network Analysis

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|³), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.     

'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns

Background  

Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings.     

A Bayesian missing value estimation method for gene expression profile data

MOTIVATION: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the treatment of missing values, this problem has received little attention. There are many options for dealing with missing values, each of which reaches drastically different results. Ignoring missing values is the simplest method and is frequently applied. This approach, however, has its flaws. In this article, we propose an estimation method for missing values, which is based on Bayesian principal component analysis (BPCA). Although the methodology that a probabilistic model and latent variables are estimated simultaneously within the framework of Bayes inference is not new in principle, actual BPCA implementation that makes it possible to estimate arbitrary missing variables is new in terms of statistical methodology. RESULTS: When applied to DNA microarray data from various experimental conditions, the BPCA method exhibited markedly better estimation ability than other recently proposed methods, such as singular value decomposition and K-nearest neighbors. While the estimation performance of existing methods depends on model parameters whose determination is difficult, our BPCA method is free from this difficulty. Accordingly, the BPCA method provides accurate and convenient estimation for missing values. AVAILABILITY: The software is available at http://hawaii.aist-nara.ac.jp/~shige-o/tools/.