基因网络研究进展

分子生物学的深入发展揭示了复杂的生命现象是大量基因相互作用的结果,传统的以描述为主的生物学和分解分析的研究方法受到挑战.随着DNA芯片和分子阵列技术的应用,快速检测生物基因组的表达已成为可能.在生命科学领域,基因网络作为一种系统的、定量的研究方法正在受到重视,该方法建立在分子生物学、非线性数学和信息学等多学科交叉的基础上.基因网络是动力系统模型,具有稳定性、层次性等一系列非线性系统的特性.通过基因表达的大量数据,结合一定的分析和计算方法可以构建合适的基因网络拓扑结构模拟系统的行为.反过来,利用已建立的基因网络可以指导进一步的实验.计算机工具和Internet资源是基因网络研究的重要手段.基因网络研究将在后基因组研究中发挥重要的作用.     

遗传基因组学(Genetical genomics)的研究进展

遗传基因组学(geneticalgenomics)是将微阵列技术和数量性状座位(QTL)分析结合起来,全基因组水平上定位基因表达的QTL(eQTL).它为研究复杂性状的分子机理和调控网络提供全新的手段.遗传基因组这个概念和研究策略在2001年由Janson和Nap首先提出,到目前为止,遗传基因组学已应用于酵母、老鼠、人以及玉米等植物.研究结果表明:基因表达水平的差异是可遗传的复杂性状;eQTL可以分为顺式作用eQTL和反式作用eQTL,顺式作用eQTL就是某个基因的eQTL定位到该基因所在的基因组区域,表明可能是该基因本身的差别引起mRNA水平的差别,反式作用就是eQTL定位到其他基因组区域,表明其他基因的差别控制该基因mRNA水平的差异.将eQTL结果、基因功能注解以及多种统计分析方法相结合,不仅能更准确地鉴别控制复杂性状及其相关基因表达的候选基因,而且能构建相应的基因调控网络.     

DNA芯片技术及其在基因表达检测中的应用

随着人类基因组计划的顺利进行,预计到２００３年人类可以完全测出自身的基因组序列。届时,人类将进入后基因组时代。探明人类全部基因的结构及其表达调控将成为后基因组时代的一个重要目标,而进行基因表达的检测则是研究基因功能的一个重要环节。人类如此浩翰的基因,...     

Adiponectin 基因剔除LacZ基因敲入小鼠模型的建立

adiponectin是脂肪细胞特异分泌的一种活性蛋白质,具有增加胰岛素敏感性、抗炎及抗动脉硬化等活性.建立adiponectin基因剔除β-半乳糖苷酶基因(LacZ)敲入小鼠模型,可为整体动物水平研究adiponectin基因功能及其表达调控机制等提供理想工具.根据生物信息学方法获得adiponectin基因组序列,设计基因剔除及敲入策略,在adiponectin基因第2和第3号外显子剔除的同时,在其ATG和信号肽序列后顺接LacZ基因完整编码序列,构建完成了Adipo-LacZ-XpPNT基因剔除质粒.通过电穿孔将打靶质粒转入ES细胞,以G418和ganciclovir进行药物筛选,获得药物抗性的ES细胞克隆,PCR和DNA印迹鉴定出正确同源重组克隆.将同源重组的ES细胞克隆注入小鼠囊胚得到嵌合体小鼠,嵌合体小鼠与C57BL/6J小鼠交配产生杂合子小鼠,杂合子间交配获得adiponectin基因剔除LacZ基因敲入纯合子小鼠.经RT-PCR、RNA印迹和ELISA检测证实纯合子小鼠脂肪和血清中adiponectin基因表达呈阴性.RT-PCR、RNA印迹及蛋白质印迹检测发现,LacZ基因在突变小鼠脂肪组织中有特异性表达,其表达谱与内源性adiponectin基因的表达谱一致.但在脂肪组织及外周血中未能检测到LacZ活性,且血清中LacZ蛋白亦呈阴性.由此成功建立了adiponectin基因完全灭活及LacZ基因以内源性adiponectin基因表达谱表达的小鼠模型,为进一步研究该基因功能及其表达调控创造了有利条件.     

基因组水平的基因挖掘与功能分析方法的探讨

为了实现基因组中特定基因功能的注释,研究者提出一种新的思路,即利用对目的基因启动子上游的顺式元件的功能的分析,进一步来推断目的基因的功能。在此主要对基因组水平的基因挖掘与功能分析方法及其研究进展进行了探讨。     

基因敲除方法及其应用

基因（gene）是核酸分子中储存信息的遗传单位,是指储存有功能的蛋白质多肽链或RNA序列信息及表达这些信息所必需的全部核苷酸序列。基因敲除（Gene knockout）是指借助分子生物学、细胞生物学和动物胚胎学的方法,通过胚胎干细胞这一特殊的中间环节将小鼠的正常功能基因的编码区破坏,使特定基因失活,以研究该基因的功能;或者通过外源基因来替换宿主基因组中相应部分,以便测定它们是否具有相同的功能,或将正常基因引入宿主基因组中置换突变基因以达到靶向基因治疗的目的。基因敲除是揭示基因功能最直接的手段之一。     

基因编辑技术在斑马鱼中的应用及研究进展

基因编辑技术通过对特定DNA片段的插入、敲除、修饰或替换等,实现对生物体中目标基因的编辑。与早期基因工程技术将遗传物质随机插入宿主基因组中的方式不同的是,基因编辑技术能够定点需要插入的位置,从而实现对生物体基因组特定位点的准确修饰、人为地改造生物体的遗传信息,目前广泛应用于斑马鱼的基因组学、遗传发育和基因功能研究中。其方法包括诱变技术、Tol2转座子、Morpholino、ZFNs、TALEN和CRISPR/Cas系统等。本研究主要介绍了基因编辑技术的作用机理与发展概况。作为一种精准而高效的基因工程方法,基因编辑技术在近年来得到了飞速地发展。它既可以采用对特定基因的靶向突变来研究基因的功能,也可以通过将功能性基因插入并替代缺陷基因而用于某些遗传性疾病的基因治疗。可以肯定的是,基因编辑技术未来将在基础生物学、医学、生物技术等多个领域具有重要的研究价值和应用价值。     

DNA芯片技术与基因表达研究

随着基因组计划的顺利实施,大量的生物信息被解析,基因表达及基因功能的研究将成为生命科学研究的热点。ＤＮＡ世片技术是近年来出现的分子生物学与微电子技术相结合的最新ＤＮＡ分析检测技术。该技术将在生命科学与信息科学之间架起一道桥梁,因而成为后基因组时代基因功能分析撮重要的技术之一。目前ＤＮＡ芯片技术已在基因保得到广泛的应用。     

时间延迟基因调控网络重构的决策树方法研究

基因调控网络的重构是功能基因组中最具挑战性的课题之一. 针对基因间转录调控的时间延迟性, 提出了一种寻找时间延迟调控关系的方法: 多点延迟调控网络算法, 简称TdGRN (time-delayed gene regulatory networking). 该方法根据时间序列基因表达谱数据, 构建时间延迟基因表达矩阵, 利用有监督决策树分类器方法和随机重排技术挖掘基因之间的时间延迟调控关系, 从而构建时间延迟的基因调控网络. 该方法是一种不依赖模型的基因网络重建方法, 相对于目前采用的基于模型的网络重建方法有显著优势, 可直接利用连续的基因表达谱数据发现延迟任一时间单位差的基因表达调控关系, 并避免了目前一些研究方法中需要人为设定基因的最大调控子数目(k)的问题. 将该方法应用于酿酒酵母细胞周期的基因表达谱数据, 并构建时间延迟的基因调控网络, 结果发现多数时间延迟调控关系获得了已有知识的支持.     

RASSF1A对黑色素瘤A375细胞基因网络的影响

RASSF1A(Ras association domain family 1 isoform A)是定位于染色体3p21.3区域的抑瘤基因,编码一个由340个氨基酸残基构成的微管相关蛋白.该基因在包括恶性黑色素瘤在内的多种肿瘤中因启动子高甲基化而表达沉默.本研究建立了RASSF1A稳定表达的恶性黑色素瘤A375细胞系,通过全基因组表达谱基因芯片分析RASSF1A过表达对A375细胞基因表达谱的影响,发现RASSF1A引起184个基因表达上调,26个基因表达下调.通过Realtime RT-PCR对部分差异表达基因进行验证,结果表明与芯片筛选结果一致.RASSF1A影响的差异表达基因功能上归属于细胞生长与增殖、细胞周期、细胞凋亡、细胞间黏附、信号传导等生物过程.采用STRING软件构建了RASSF1A影响的差异表达基因调控网络,结果表明RASSF1A调控的差异表达基因构成一个高连接度的基因网络.其中,炎症细胞因子、转录因子位于网络中央.RASSF1A通过影响炎症细胞因子与转录因子之间的表达,影响A375细胞基因网络,调节黑色素瘤恶性生物学行为.     

eXPatGen: generating dynamic expression patterns for the systematic evaluation of analytical methods

MOTIVATION: Experimental gene expression data sets, such as those generated by microarray or gene chip experiments, typically have significant noise and complicated interconnectivities that make understanding even simple regulatory patterns difficult. Given these complications, characterizing the effectiveness of different analysis techniques to uncover network groups and structures remains a challenge. Generating simulated expression patterns with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of different analysis methods. A simulation-based approach can systematically evaluate different gene expression analysis techniques and provide a basis for improved methods in dynamic metabolic network reconstruction. RESULTS: We have developed an on-line simulator, called eXPatGen, to generate dynamic gene expression patterns typical of microarray experiments. eXPatGen provides a quantitative network structure to represent key biological features, including the induction, repression, and cascade regulation of messenger RNA (mRNA). The simulation is modular such that the expression model can be replaced with other representations, depending on the level of biological detail required by the user. Two example gene networks, of 25 and 100 genes respectively, were simulated. Two standard analysis techniques, clustering and PCA analysis, were performed on the resulting expression patterns in order to demonstrate how the simulator might be used to evaluate different analysis methods and provide experimental guidance for biological studies of gene expression. AVAILABILITY: http://www.che.udel.edu/eXPatGen/     

Annotating gene function by combining expression data with a modular gene network

MOTIVATION: A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks. RESULTS: We present a systematic method for gene clustering by combining these totally different two types of data, particularly focusing on network modularity, a global feature of gene networks. Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network. Our learning algorithm which minimizes an energy function considering the network modularity is practically time-efficient, regardless of using the global network property. We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters. Experimental results showed that our method outperformed other four competing methods, including k-means and existing graph partitioning methods, being statistically significant in all cases. Further detailed analysis showed that our method could group a set of genes into a cluster which corresponds to the folate metabolic pathway while other methods could not. From these results, we can say that our method is highly effective for gene clustering and annotating gene function.     

Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network

The vast majority (>95%) of single-gene mutations in yeast affect not only the expression of the mutant gene, but also the expression of many other genes. These data suggest the presence of a previously uncharacterized "gene expression network"--a set of interactions between genes which dictate gene expression in the native cell environment. Here, we quantitatively analyze the gene expression network revealed by microarray expression data from 273 different yeast gene deletion mutants.(1) We find that gene expression interactions form a robust, error-tolerant "scale-free" network, similar to metabolic pathways(2) and artificial networks such as power grids and the internet.(3-5) Because the connectivity between genes in the gene expression network is unevenly distributed, a scale-free organization helps make organisms resistant to the deleterious effects of mutation, and is thus highly adaptive. The existence of a gene expression network poses practical considerations for the study of gene function, since most mutant phenotypes are the result of changes in the expression of many genes. Using principles of scale-free network topology, we propose that fragmenting the gene expression network via "genome-engineering" may be a viable and practical approach to isolating gene function.     

Proteomics: a link between genomics,genetics and physiology

Thanks to spectacular advances in the techniques for identifying proteins separated by two-dimensional electrophoresis and in methods for large-scale analysis of proteome variations, proteomics is becoming an essential methodology in various fields of plant biology. In the study of pleiotropic effects of mutants and in the analysis of responses to hormones and to environmental changes, the identification of involved metabolic pathways can be deduced from the function of affected proteins. In molecular quantitative genetics, proteomics can be used to map translated genes and loci controlling their expression, which can be used to identify proteins accounting for the variation of complex phenotypic traits. Linking gene expression to cell metabolism on the one hand and to genetic maps on the other, proteomics is a central tool for functional genomics.     

Seeing is believing: imaging techniques to monitor plant health.

Historically, early stress-induced changes in plants have been mainly detected after destructive sampling followed by biochemical and molecular determinations. Imaging techniques that allow immediate detection of stress-situations, before visual symptoms appear and adverse effects become established, are emerging as promising tools for crop yield management. Such monitoring approaches can also be applied to screen plant populations for mutants with increased stress tolerance. At the laboratory scale, different imaging methods can be tested and one or a combination best suited for crop surveillance chosen. The system of choice can be applied under controlled laboratory conditions to guide selective sampling for the molecular characterisation of rapid stress-induced changes. Such an approach permits to isolate presymptomatically induced genes, or to obtain a panoramic view of early gene expression using gene-arrays when plants undergo physiological changes undetected by the human eye. Using this knowledge, plants can be engineered to be more stress resistant, and tested for field performance by the same methodologies. In ongoing efforts of genome characterisation, genes of unknown function are revealed at an ever-accelerating pace. By monitoring changes in phenotypic characteristics of transgenic plants expressing those genes, imaging techniques could help to identify their function.     

Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns

MOTIVATIONS AND RESULTS: Gene groups that are significantly related to a disease can be detected by conducting a series of gene expression experiments. This work is aimed at discovering special types of gene groups that satisfy the following property. In each group, its member genes are found to be one-to-one contained in pre-determined intervals of gene expression level with a large frequency in one class of cells but are never found unanimously in these intervals in the other class of cells. We call these gene groups emerging patterns, to emphasize the patterns' frequency changes between two classes of cells. We use effective discretization and gene selection methods to obtain the most discriminatory genes. We also use efficient algorithms to derive the patterns from these genes. According to our studies on the ALL/AML dataset and the colon tumor dataset, some patterns, which consist of one or more genes, can reach a high frequency of 90%, or even 100%. In other words, they nearly or fully dominate one class of cells, even though they rarely occur in the other class. The discovered patterns are used to classify new cells with a higher accuracy than other reported methods. Based on these patterns, we also conjecture the possibility of a personalized treatment plan which converts colon tumor cells into normal cells by modulating the expression levels of a few genes.     

Discovering gene networks with a neural-genetic hybrid

Recent advances in biology (namely, DNA arrays) allow an unprecedented view of the biochemical mechanisms contained within a cell. However, this technology raises new challenges for computer scientists and biologists alike, as the data created by these arrays is often highly complex. One of the challenges is the elucidation of the regulatory connections and interactions between genes, proteins and other gene products. In this paper, a novel method is described for determining gene interactions in temporal gene expression data using genetic algorithms combined with a neural network component. Experiments conducted on real-world temporal gene expression data sets confirm that the approach is capable of finding gene networks that fit the data. A further repeated approach shows that those genes significantly involved in interaction with other genes can be highlighted and hypothetical gene networks and circuits proposed for further laboratory testing.     

Gene expression profiling of the rewarding effect caused by methamphetamine in the mesolimbic dopamine system

Methamphetamine, a commonly used addictive drug, is a powerful addictive stimulant that dramatically affects the CNS. Repeated METH administration leads to a rewarding effect in a state of addiction that includes sensitization, dependence, and other phenomena. It is well known that susceptibility to the development of addiction is influenced by sources of reinforcement, variable neuroadaptive mechanisms, and neurochemical changes that together lead to altered homeostasis of the brain reward system. These behavioral abnormalities reflect neuroadaptive changes in signal transduction function and cellular gene expression produced by repeated drug exposure. To provide a better understanding of addiction and the mechanism of the rewarding effect, it is important to identify related genes. In the present study, we performed gene expression profiling using microarray analysis in a reward effect animal model. We also investigated gene expression in four important regions of the brain, the nucleus accumbens, striatum, hippocampus, and cingulated cortex, and analyzed the data by two clustering methods. Genes related to signaling pathways including G-protein-coupled receptor-related pathways predominated among the identified genes. The genes identified in our study may contribute to the development of a gene modeling network for methamphetamine addiction.     

A Bayesian network classification methodology for gene expression data.

We present new techniques for the application of a Bayesian network learning framework to the problem of classifying gene expression data. The focus on classification permits us to develop techniques that address in several ways the complexities of learning Bayesian nets. Our classification model reduces the Bayesian network learning problem to the problem of learning multiple subnetworks, each consisting of a class label node and its set of parent genes. We argue that this classification model is more appropriate for the gene expression domain than are other structurally similar Bayesian network classification models, such as Naive Bayes and Tree Augmented Naive Bayes (TAN), because our model is consistent with prior domain experience suggesting that a relatively small number of genes, taken in different combinations, is required to predict most clinical classes of interest. Within this framework, we consider two different approaches to identifying parent sets which are supported by the gene expression observations and any other currently available evidence. One approach employs a simple greedy algorithm to search the universe of all genes; the second approach develops and applies a gene selection algorithm whose results are incorporated as a prior to enable an exhaustive search for parent sets over a restricted universe of genes. Two other significant contributions are the construction of classifiers from multiple, competing Bayesian network hypotheses and algorithmic methods for normalizing and binning gene expression data in the absence of prior expert knowledge. Our classifiers are developed under a cross validation regimen and then validated on corresponding out-of-sample test sets. The classifiers attain a classification rate in excess of 90% on out-of-sample test sets for two publicly available datasets. We present an extensive compilation of results reported in the literature for other classification methods run against these same two datasets. Our results are comparable to, or better than, any we have found reported for these two sets, when a train-test protocol as stringent as ours is followed.