蛋白互作与基因共调控的关联研究

目的 通过分析2000个人类结肠癌相关基因以及GO( gene ontology)数据库中与人类生物学过程相关的25 189个基因,揭示蛋白互作比率与基因共调控强度之间的关联趋势.方法 基于四联密码子算法提取既包含蛋白互作关系又包含共调控关系的混合模式.应用超几何分布(P≤0.05)筛选出频数大于等于5且具有统计学意义的混合模式1073个.结果 蛋白互作比率与基因共调控强度之间存在明显的正相关趋势,即随着蛋白质互作比例的增加,相关基因共调控的强度也随之增强.结论 互作蛋白(基因)倾向于被共调控.     

基于非负矩阵分解的大脑不同区域基因表达数据分析

基因之间存在多种多样的表达调控活动,一般认为这些调控关系隐含在基因表达谱中。针对阿尔茨海默症(AD)起病隐匿、诊断难、发病机理复杂以及基因信号传导通路和调控关系难以重建等特征,利用非平滑非负矩阵分解(nsNMF)方法提取AD致病基因,聚类过程中利用共表型相关性系数(CCC)选取聚类数k的值,得到最优的聚类数目。针对基因表达数据噪声高、信息变量隐藏难分析的困难,考虑AD的发生发展与许多大脑功能区域密切相关的特性,提出将nsNMF分别应用于AD患者的大脑海马区、内嗅区皮质、颞中回及视觉皮层区的基因表达数据中,共提取3 800个显著基因,其中包括确定与AD致病机理有关联的10个致病基因,并进行了生物学分析,得到了AD相关的细胞凋亡、代谢及炎症反应等生物过程,显示nsNMF方法及大脑多区域数据集的联合分析能更全面地探寻AD信号传导关系及基因调控方式。     

S系统及时序布尔网络模型构建鼠疫耶尔森菌基因调控网络的初步研究

目的应用S幂率系统构建鼠疫耶尔森菌小尺度基因调控网络。方法收集鼠疫耶尔森菌基因时间序列表达数据,数据经预处理后,分别用布尔网络模型和S幂率系统网络模型构建小尺度基因调控网络,并对两个网络结构进行比较。结果应用布尔网络模型构建的小尺度基因调控网络中存在2种基因调控模式,而应用S幂率系统网络模型构建的小尺度基因调控网络中存在4种基因调控模式。结论 S幂率系统可用于构建基因调控网络模型。     

利用转录因子结合位点的基因调控区序列比较

利用DNA中转录因子结合位点分布的序列比较方法对DNA序列进行聚类,并分析基因之间的联系.运用Matlab工具结合TRANSFAC数据库中的数据,对一组基因芯片共调控基因的上游序列进行比较和聚类,获得能够反映基因关系的树状聚类结果,从中确定出具有共同功能特征的基因,揭示了在大骨节病相关的诸多基因中,基因CIDEA、CYP4V2、RHBDD3、ENC1的调控区域有共同序列特征,表达模式和调控机理最为相似.这为更深层次的基因功能分析提供了依据.     

细胞信号转导与可变剪接调控

大多数真核基因能够发生可变剪接,其调控对于生理和病理状态下细胞功能的实现至关重要,而异常可变剪接则可导致多种疾病.虽然已知可变剪接能够在转录后水平调节基因表达,然而目前仍不清楚特定的可变剪接模式是如何被调控的.越来越多的研究发现细胞信号和外界环境刺激能够调控靶基因的剪接模式,并且已发现一些与可变剪接调控有关的信号转导通路,而后者能够通过修饰剪接因子进而改变剪接因子的亚细胞定位或者活性,从而实现对靶基因可变剪接模式的调控.由细胞信号转导通路所构成的网络能够灵活多样地调控基因剪接,一条信号通路可调控多个基因剪接,而多条信号通路也可调控同一基因剪接,对于理解信号转导过程的分子机制具有重要意义.     

基于创新的四维密码方法分析共调控与蛋白互作关系

【摘要】目的 基于创新的四维密码子的方法挖掘共调控且互作的基因对，研究共调控与蛋白互作之间的关联趋势。方法 我们提出了创新的基于四维密码子的关联分析方法，并把此方法应用于与结肠癌相关的2000个疾病基因。结果 发现共调控的互作蛋白对比随机情况下显著；随着共调控强度的增强，蛋白间互作趋势越来越明显。结论 基于创新的四维密码子的关联分析方法，能够研究共调控与蛋白互作的关联趋势。     

TGF-β/Smad信号转导通路及其调控机制的研究进展

转化生长因子-β(TGF-β)超家族在胚胎发育和细胞的分化中具有多方面的生理功能,调控基因十分广泛.Smads蛋白是TGF-β超家族表达细胞内重要的信号转导和调控分子.多种DNA结合蛋白可以与Smads结合,直接影响其所调控基因的差异.Smads蛋白在DNA共结合因子辅助下,招募转录激活因子或转录抑制因子调控靶基因的转录.     

头颈部鳞癌中TP53基因的调控网络分析

目的挖掘隐藏在头颈部鳞癌中抑癌基因TP53表达异常的机制。方法从TCGA数据库中选取279例头颈部鳞癌样本的测序数据,利用cbioportal获得TP53基因在头颈部鳞癌中的表达情况及其对患者生存的影响,并筛选样本中与TP53存在共表达关系的基因。利用String和DAVID数据库,建立TP53的共表达网络并分析网络功能,通过lncRNA2target和Star Base数据库,筛选出能够调控TP53的短链非编码RNA(microRNA),长链非编码RNA(lncRNA)和竞争性内源性RNA(ceRNA),利用Cytoscape软件建立TP53相关基因调控网络。结果 279例头颈部鳞癌样本中共有77%的样本出现TP53表达异常,且TP53表达异常患者的生存率显著低于TP53正常表达的患者。此外,筛选出TP53基因的共表达基因288个,(Spearman分数和Pearson分数0.3)功能分析结果显示共表达基因的功能主要为转录调控、细胞凋亡、增殖过程和免疫应答等功能。TP53基因调控网络揭示has-miR-186-5p和has-miR-202-3p等43个microRNA能够靶向调控TP53基因;此外,MEG1、TUG1和MALAT1等6个lncRNA,PHC2、FZD4和TUB等56个mRNA能够通过ceRNA作用调控TP53。结论通过生物信息学方法和基因数据库建立的TP53相关基因调控网络揭示可能调控抑癌基因TP53的若干microRNA、lncRNA和ceRNA,可以为头颈部鳞癌中TP53的作用机制和以TP53为靶点的分子治疗提供新的切入点。     

CD226基因5''''上游调控序列研究

目的：研究CD226基因5’上游调控序列对CD226基因表达调控的影响。方法：通过基因克隆的方法将CD226基因5’上游调控序列，克隆到荧光素酶报告基因载体中（pGL3-basic），用脂质体转染Jurkat细胞，48小时后检测荧光素酶活性。结果：CD226基因存在两个启动子P1和P2，分别位于与-843--319bp和＋1-＋181bp，PMA可以上调P1的启动子活性，对P2有一定的抑制作用；A23187均可以上调两个启动子的活性，但对P2的作用更为明显。结论：CD226基因存在两个启动子，其活性受到PMA和A23187的调控，并呈与蛋白水平表达调控相类似的模式。     

长链非编码RNA在表观遗传学中的研究进展

表观遗传学是基于遗传学基础之上发展起来的生物学分支,研究发现人类很多疾病与表观遗传调控相关,其主要机制包括:染色质重塑、组蛋白修饰,基因组印记及非编码RNA(ncRNA)调控.而长链非编码RNA(lncRNA)是非编码RNA中的一类,不仅可以通过与靶基因直接结合调控靶基因的转录,还能募集调控因子,参与基因的沉默,在表观遗传调控中起着重要作用.     

Sequential patterns mining and gene sequence visualization to discover novelty from microarray data

Data mining allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyze by end users. In this paper, we focus on sequential pattern mining and propose a new visualization system to help end users analyze the extracted knowledge and to highlight novelty according to databases of referenced biological documents. Our system is based on three visualization techniques: clouds, solar systems, and treemaps. We show that these techniques are very helpful for identifying associations and hierarchical relationships between patterns among related documents. Sequential patterns extracted from gene data using our system were successfully evaluated by two biology laboratories working on Alzheimer’s disease and cancer.     

SLAM: cross-species gene finding and alignment with a generalized pair hidden Markov model

Comparative-based gene recognition is driven by the principle that conserved regions between related organisms are more likely than divergent regions to be coding. We describe a probabilistic framework for gene structure and alignment that can be used to simultaneously find both the gene structure and alignment of two syntenic genomic regions. A key feature of the method is the ability to enhance gene predictions by finding the best alignment between two syntenic sequences, while at the same time finding biologically meaningful alignments that preserve the correspondence between coding exons. Our probabilistic framework is the generalized pair hidden Markov model, a hybrid of (1). generalized hidden Markov models, which have been used previously for gene finding, and (2). pair hidden Markov models, which have applications to sequence alignment. We have built a gene finding and alignment program called SLAM, which aligns and identifies complete exon/intron structures of genes in two related but unannotated sequences of DNA. SLAM is able to reliably predict gene structures for any suitably related pair of organisms, most notably with fewer false-positive predictions compared to previous methods (examples are provided for Homo sapiens/Mus musculus and Plasmodium falciparum/Plasmodium vivax comparisons). Accuracy is obtained by distinguishing conserved noncoding sequence (CNS) from conserved coding sequence. CNS annotation is a novel feature of SLAM and may be useful for the annotation of UTRs, regulatory elements, and other noncoding features.     

基于流形学习的聚类方法在基因芯片表达谱分析中的应用

对基因芯片表达谱的聚类分析有助于发现共表达的基因,而共表达的特性往往是共调控基因所拥有的性质。因此,对基因表达谱的准确聚类将有利于更加准确地发现基因之间的调控关系。本研究使用机器学习中的等度规映射、局部线性嵌入、拉普拉斯特征根映射等流形学习方法处理基因表达谱数据,得到非线性降维后的数据。在此基础上应用K均值聚类、模糊聚类、自组织映射神经网络等聚类方法,根据给定的阈值,从酵母基因表达数据的382个聚类结果中得到了117个共表达基因对,而从人类血清组织细胞的基因表达数据的132个聚类结果中得到了89个共表达基因对。使用的判别准则表明,基于流形学习的聚类方法与以往的方法相当,且能够被用以发现高维基因芯片表达数据中的低维的流形结构。     

用主成分分析探索基因表达模式

以时间点(阵列)为变量,对酵母时间表达序列进行主成分分析,提取出三个主成分:第一主成分代表整个酵母细胞周期中平稳的基因表达状态,第二主成分代表酵母细胞周期中lateG1期和M期差异的基因表达状态,第三主成分表示的是earlyG1期和S/G2期差异的基因表达状态。由第二、和第三主成分揭示出基因表达谱中四个周期性基因表达模式:分别对应lateG1期、M期、earlyG1期和S/G2期,而且发现了一批表达水平呈周期性变化的基因,为进一步研究基因周期性表达和基因调控网络提供有价值的指导。     

Spatial patterning of gene expression using surface-immobilized recombinant adenovirus

Spatially patterned gene expression drives tissue organization and is a critical determinant of tissue function. Approaches in functional tissue engineering will require not only the spatial organization of cells but also control of their gene expression patterns. We report a method to generate patterns of gene expression within a monolayer of cells by using surface-immobilized recombinant adenovirus. This study represents a new approach to engineering tissues that relies on controlling spatial patterns of gene expression, and can be used independently or in combination with positioning of different cell types. This technique may have broad applications in biotechnology including tissue engineering and targeted gene delivery.     

Cancer classification and prediction using logistic regression with Bayesian gene selection

In microarray-based cancer classification and prediction, gene selection is an important research problem owing to the large number of genes and the small number of experimental conditions. In this paper, we propose a Bayesian approach to gene selection and classification using the logistic regression model. The basic idea of our approach is in conjunction with a logistic regression model to relate the gene expression with the class labels. We use Gibbs sampling and Markov chain Monte Carlo (MCMC) methods to discover important genes. To implement Gibbs Sampler and MCMC search, we derive a posterior distribution of selected genes given the observed data. After the important genes are identified, the same logistic regression model is then used for cancer classification and prediction. Issues for efficient implementation for the proposed method are discussed. The proposed method is evaluated against several large microarray data sets, including hereditary breast cancer, small round blue-cell tumors, and acute leukemia. The results show that the method can effectively identify important genes consistent with the known biological findings while the accuracy of the classification is also high. Finally, the robustness and sensitivity properties of the proposed method are also investigated.     

Knowledge-assisted recognition of cluster boundaries in gene expression data

BACKGROUND AND MOTIVATION: DNA microarray technology has made it possible to determine the expression levels of thousands of genes in parallel under multiple experimental conditions. Genome-wide analyses using DNA microarrays make a great contribution to the exploration of the dynamic state of genetic networks, and further lead to the development of new disease diagnosis technologies. An important step in the analysis of gene expression data is to classify genes with similar expression patterns into the same groups. To this end, hierarchical clustering algorithms have been widely used. Major advantages of hierarchical clustering algorithms are that investigators do not need to specify the number of clusters in advance and results are presented visually in the form of a dendrogram. However, since traditional hierarchical clustering methods simply provide results on the statistical characteristics of expression data, biological interpretations of the resulting clusters are not easy, and it requires laborious tasks to unveil hidden biological processes regulated by members in the clusters. Therefore, it has been a very difficult routine for experts. OBJECTIVE: Here, we propose a novel algorithm in which cluster boundaries are determined by referring to functional annotations stored in genome databases. MATERIALS AND METHODS: The algorithm first performs hierarchical clustering of gene expression profiles. Then, the cluster boundaries are determined by the Variance Inflation Factor among the Gene Function Vectors, which represents distributions of gene functions in each cluster. Our algorithm automatically specifies a cutoff that leads to functionally independent agglomerations of genes on the dendrogram derived from similarities among gene expression patterns. Finally, each cluster is annotated according to dominant gene functions within the respective cluster. RESULTS AND CONCLUSIONS: In this paper, we apply our algorithm to two gene expression datasets related to cell cycle and cold stress response in budding yeast Saccharomyces cerevisiae. As a result, we show that the algorithm enables us to recognize cluster boundaries characterizing fundamental biological processes such as the Early G1, Late G1, S, G2 and M phases in cell cycles, and also provides novel annotation information that has not been obtained by traditional hierarchical clustering methods. In addition, using formal cluster validity indices, high validity of our algorithm is verified by the comparison through other popular clustering algorithms, K-means, self-organizing map and AutoClass.     

基于PCA和LDA方法的肿瘤基因表达谱数据分类

目的基因芯片技术对医学临床诊断、治疗、药物开发和筛选等技术的发展具有革命性的影响。针对高维医学数据降维困难及基因表达谱样本数据少、维度高、噪声大的特点,维数约减十分必要。基于主成分分析（principalcomponentanalysis,PCA）和线性判别分析（1ineardiscriminantanalysis,LDA）方法,有效解决了基因表达谱数据分类问题,并提高了识别率。方法分别引人PCA和LDA方法对基因表达谱数据进行降维,然后用K近邻（K—nearestneighbor,KNN）作为分类器对数据进行分类,并分别在乳腺癌和卵巢癌质谱数据上。结果在两类癌症质谱数据上应用PCA和LDA方法能够有效提取分类特征信息,并在保持较高分类正确率的前提下大幅度降低医学数据的维数。结论利用维数约减的方法对癌症基因表达谱数据进行分类,可辅助临床医生发现新的疾病特征,提高疾病诊断的正确率。     

Pattern robustness of diagnostic gene expression signatures in leukemia

Microarray technology has been proposed as an addition to the methods in current use for diagnosing leukemia. Before a new technology can be used in a diagnostic setting, the method has to be shown to produce robust results. It is known that, given the technical aspects of specimen sampling and target preparation, global gene expression patterns can change dramatically. Various parameters such as RNA degradation, shipment time, sample purity, and patient age can principally influence measured gene expression. However, thus far, no information has been available on the robustness of a diagnostic gene expression signature. We demonstrate here that for a subset of acute leukemia, expression profiling is applicable in a diagnostic setting, considering various influencing parameters. With the use of a set of differentially expressed genes, that is, a diagnostic gene expression signature, four genetically defined acute myeloid leukemia subtypes with recurrent chromosomal aberrations can clearly be identified. In addition, we show that preparation by different operators and using different sample-handling procedures did not impair the robustness of diagnostic expression signatures. In conclusion, our results provide additional support for the applicability of microarrays in a diagnostic setting, and we have been encouraged to enroll patients in a prospective study in which microarrays will be tested as an additional routine diagnostic method in parallel with standard diagnostic procedures.