家族性肾阳虚的基因功能富集分析

在临床调查中筛选出一个典型肾阳虚证家系,选取典型肾阳虚证患者3例及家系内正常人3例进行表达谱芯片试验．芯片分析结果显示,肾阳虚证者表达上调基因106条,下调基因16条．通过基因功能分类及深入的基因功能富集分析发现,肾阳虚证与GnRH信号通路及氧化磷酸化密切相关（P≤005）,从而为肾阳虚证本质的研究提供了新的思路,但具体的机制有待深入研究．     

猪链球菌2型全基因组DNA芯片的研制及基因表达谱技术平台的建立

目的：研制猪链球菌2型（SS2）全基因组DNA芯片,建立SS2基因表达谱技术平台。方法：利用SS2全基因组序列,挑选出2194条基因,经PCR扩增出2156条基因并将产物纯化,点样制备芯片;将芯片用于表达谱研究,采用实时定量PCR验证表达谱结果,对芯片进行可靠性分析。结果：芯片杂交数据与实时定量PCR验证显示了较高的相关性,二者相关系数r=0.87。结论：研制了一批SS2全基因组DNA芯片,并建立了基于DNA芯片的表达谱技术平台。     

采用基于Gene Ontology的聚类方法研究白血病的遗传异质性

采用基因表达谱可以研究基因功能模块与疾病异质性之间的关系.根据两套白血病基因表达谱数据,将富集高变异基因的Gene Ontology基因功能模块作为特征功能模块,将疾病样本聚为两类.通过对比原始多类标签,采用聚类评估指标来分析两类化聚类结果的效果,并探讨特征功能模块与疾病异质性之间的关系.实验结果显示:在两套不同的白血病基因表达谱数据中得到的特征功能模块类似,它们对白血病亚型有较强的分型能力.     

生物信息学方法在基因芯片数据功能分析中的应用

基因芯片技术是当前功能基因组研究的重要工具.基因功能分析是将基因表达数据与基因功能或生物学通路相联系,寻找有意义的变化基因.本文介绍了GO分类法、信号通路和生物调控网络等常用的基因功能分析方法和工具.     

三棱内酯B调控人冠状动脉内皮细胞基因表达谱的生物信息学分析

目的:分析三棱内酯B在人冠状动脉内皮细胞中的表达谱数据集,寻找三棱内酯B调控血管内皮功能的关键作用靶点。方法:基于GEO公共数据库,下载原始表达谱数据集(GSE44598),经过差异基因筛选,功能注释,通路富集,信号通路网络以及基因互作网络分析,找出三棱内酯B对人冠状动脉内皮细胞基因表达谱产生影响的关键基因和信号通路。结果:同对照组相比,三棱内酯B给药组共有5224个基因有显著性差异,包括2628个上调基因和2596个下调基因。基因功能注释和信号通路富集分析表明,差异基因主要参与了细胞周期过程。网络分析显示,MAPK信号通路、细胞周期通路以及PLCG2,PRKACA和ADCY4等为关键信号通路和基因。结论:三棱内酯B通过影响PLCG2,PRKACA,ADCY4等基因的表达,参与MAPK和细胞周期等信号通路,从而调节人冠状内皮细胞的功能。这些关键基因和信号通路是三棱内酯B在心血管疾病治疗应用中潜在的作用靶点。     

特征代谢通路上的基因表达相关性及共调控表达模式

利用随机森林-通路分析法,通过袋外样本OOB的分类错误率筛选特征代谢通路,在特征通路上作基因表达相关性研究并对通路上的基因采用MAP(Mining attribute profile)算法挖掘不同实验条件下基因的共调控表达模式,对共调控表达模式进行聚类.分析结果显示同一特征代谢通路上的基因表达倾向相似,有2条特征代谢通路存在共表达模式.其中一条通路含108个表达模式,对这些模式进行聚类,其最低聚类的相似系数仍高达0.623.说明同一特征代谢通路上的基因共表达模式在不同实验条件下仍具有高度的相似性.对以通路作为基因模块进行复杂疾病的研究具有借鉴意义.     

弥漫大B细胞淋巴瘤1号染色体的基因表达分析

目的:探讨弥漫大B细胞淋巴瘤(Diffuse Large B-Cell Lymphoma,DLBCL)中1号染色体基因表达情况。方法:采用激光显微切割技术分离临床DLBCL病人淋巴结标本中的淋巴细胞,提取淋巴细胞的mRNA并与表达谱芯片杂交,通过信号扫描、处理后获得表达基因杂交信号强度。每基因设11-20对探针。杂交信号与错配探针对比,扣除背景值后,使用Wilcoxon符号秩和检验选取与错配杂交信号有显著差异的基因作为分析结果(P=0.05)。然后随机选取四个检测到的基因,使用PCR方法检验基因芯片结果的可靠性。结果:成功地从快速冷冻保存的DLBCL标本中提取RNA。使用表达谱芯片进行研究,发现了共316条1号染色体编码的基因在DLBCL细胞中表达。根据胞内定位,基因功能和基因所属的代谢通路三种分类方法对所得基因进行分类分析。基因表达密度分析显示DLBCL中1号染色体上的基因表达情况与编码基因分布情况存在统计学差异。结论:使用表达谱芯片研究了DLBCL中1号染色体上的基因表达情况。     

弥漫大B 淋巴瘤12 号染色体基因表达的研究

目的:研究弥漫大B淋巴瘤(Diffuse Large B-Cell Lymphoma,DLBCL)12号染色体基因表达情况。方法:收取临床DLBCL病人淋巴结标本液氮速冻,快速冷冻切片,采用激光显微切割技术分离单纯淋巴瘤细胞,提取淋巴瘤细胞中的mRNA与表达谱芯片杂交,通过信号扫描、处理后获得表达基因杂交信号强度。每基因设11-20对探针。杂交信号与错配探针对比,扣除背景值后,使用Wilcoxon符号秩和检验选取与错配杂交信号有显著差异的基因作为分析结果(P=0.05)。随机选取两个检测到的基因,使用PCR方法检验基因芯片结果的可靠性。结果:成功地从快速冷冻保存的DLBCL标本中提取了RNA。使用表达谱芯片进行研究,发现了共164条12号染色体编码的基因在淋巴瘤细胞中表达。并根据胞内定位,基因功能和基因所属的代谢通路三种分类方法对所得基因进行分类分析。基因表达密度分析显示12号染色体上的基因表达情况与编码基因分布情况比较一致。结论:使用表达谱芯片研究了12号染色体上的基因表达情况,为研究DLBCL提供了依据。     

利用RNA-seq技术筛选大白猪皮下和肌内脂肪组织差异表达基因

为了比较分析大白猪皮下和肌内脂肪组织的全转录组数据,探究调控脂肪沉积的分子机制,本文采用RNA-seq技术和生物信息学方法鉴定大白猪皮下和肌内脂肪组织基因表达谱,对差异表达基因进行GO(Gene Ontology)分析、信号通路富集分析以及蛋白互作网络分析。大白猪皮下和肌内脂肪组织中有180个基因差异表达,上调基因主要参与细胞增殖、脂质激酶活性和磷脂代谢等与脂质代谢相关的生物学过程正调控,下调基因显著富集于脂肪细胞分化中起重要调控作用的MAPK信号转导通路。差异表达基因主要通过参与脂质代谢及通过MAPK信号转导通路调控脂肪细胞成脂分化,进而影响大白猪皮下和肌内脂肪的沉积。     

一种融合表达谱相关性信息的激活子网辨识算法

传统表达谱数据分析方法集中于寻找差异表达基因和共表达基因集合,没有考虑基因表达产物之间已知的相互作用.近年来在系统生物学的研究中发展了将基因表达谱与蛋白质相互作用网络进行整合分析的方法.现有方法未能综合考虑基因表达差异性和相关性信息,容易导致辨识结果中重要功能分子缺失且生物学功能相关度不高.提出一种融合表达谱差异性和相关性信息的激活子网辨识算法,能够在蛋白质相互作用网络中辨识高功能相关度的激活子网.应用到人免疫缺陷病毒HIV-1感染过程的研究,结果表明,该算法可以有效避免仅考虑基因表达差异性所引入的偏差,揭示了高相关性低表达差异基因在相关通路中的关键性作用.     

A computational approach to measuring coherence of gene expression in pathways

This study uses a computational approach to analyze coherence of expression of genes in pathways. Microarray data were analyzed with respect to coherent gene expression in a group of genes defined as a pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Our hypothesis is that genes in the same pathway are more likely to be coordinately regulated than a randomly selected gene set. A correlation coefficient for each pair of genes in a pathway was estimated based on gene expression in normal or tumor samples, and statistically significant correlation coefficients were identified. The coherence indicator was defined as the ratio of the number of gene pairs in the pathway whose correlation coefficients are significant, divided by the total number of gene pairs in the pathway. We defined all genes that appeared in the KEGG pathways as a reference gene set. Our analysis indicated that the mean coherence indicator of pathways is significantly larger than the mean coherence indicator of random gene sets drawn from the reference gene set. Thus, the result supports our hypothesis. The significance of each individual pathway of n genes was evaluated by comparing its coherence indicator with coherence indicators of 1000 random permutation sets of n genes chosen from the reference gene set. We analyzed three data sets: two Affymetrix microarrays and one cDNA microarray. For each of the three data sets, statistically significant pathways were identified among all KEGG pathways. Seven of 96 pathways had a significant coherence indicator in normal tissue and 14 of 96 pathways had a significant coherence indicator in tumor tissue in all three data sets. The increase in the number of pathways with significant coherence indicators may reflect the fact that tumor cells have a higher rate of metabolism than normal cells. Five pathways involved in oxidative phosphorylation, ATP synthesis, protein synthesis, or RNA synthesis were coherent in both normal and tumor tissue, demonstrating that these are essential genes, a high level of expression of which is required regardless of cell type.     

The correlation of gene expression and co-regulated gene patterns in characteristic KEGG pathways

There is great interest in chromosome- and pathway-based techniques for genomics data analysis in the current work in order to understand the mechanism of disease. However, there are few studies addressing the abilities of machine learning methods in incorporating pathway information for analyzing microarray data. In this paper, we identified the characteristic pathways by combining the classification error rates of out-of-bag (OOB) in random forests with pathways information. At each characteristic pathway, the correlation of gene expression was studied and the co-regulated gene patterns in different biological conditions were mined by Mining Attribute Profile (MAP) algorithm. The discovered co-regulated gene patterns were clustered by the average-linkage hierarchical clustering technique. The results showed that the expression of genes at the same characteristic pathway were approximate. Furthermore, two characteristic pathways were discovered to present co-regulated gene patterns in which one contained 108 patterns and the other contained one pattern. The results of cluster analysis showed that the smallest similarity coefficient of clusters was more than 0.623, which indicated that the co-regulated patterns in different biological conditions were more approximate at the same characteristic pathway. The methods discussed in this paper can provide additional insight into the study of microarray data.     

Discovering molecular pathways from protein interaction and gene expression data

In this paper, we describe an approach for identifying 'pathways' from gene expression and protein interaction data. Our approach is based on the assumption that many pathways exhibit two properties: their genes exhibit a similar gene expression profile, and the protein products of the genes often interact. Our approach is based on a unified probabilistic model, which is learned from the data using the EM algorithm. We present results on two Saccharomyces cerevisiae gene expression data sets, combined with a binary protein interaction data set. Our results show that our approach is much more successful than other approaches at discovering both coherent functional groups and entire protein complexes.     

Biological and functional analysis of statistically significant pathways deregulated in colon cancer by using gene expression profiles

Gene expression profiling offers a great opportunity for studying multi-factor diseases and for understanding the key role of genes in mechanisms which drive a normal cell to a cancer state. Single gene analysis is insufficient to describe the complex perturbations responsible for cancer onset, progression and invasion. A deeper understanding of the mechanisms of tumorigenesis can be reached focusing on deregulation of gene sets or pathways rather than on individual genes. We apply two known and statistically well founded methods for finding pathways and biological processes deregulated in pathological conditions by analyzing gene expression profiles. In particular, we measure the amount of deregulation and assess the statistical significance of predefined pathways belonging to a curated collection (Molecular Signature Database) in a colon cancer data set. We find that pathways strongly involved in different tumors are strictly connected with colon cancer. Moreover, our experimental results show that the study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. Our study shows the importance of using gene sets rather than single genes for understanding the main biological processes and pathways involved in colorectal cancer. Our analysis evidences that many of the genes involved in these pathways are strongly associated to colorectal tumorigenesis. In this new perspective, the focus shifts from finding differentially expressed genes to identifying biological processes, cellular functions and pathways perturbed in the phenotypic conditions by analyzing genes co-expressed in a given pathway as a whole, taking into account the possible interactions among them and, more importantly, the correlation of their expression with the phenotypical conditions.     

Evaluation of light regulatory potential of Calvin cycle steps based on large-scale gene expression profiling data

Although large-scale gene expression data have been studied from many perspectives, they have not been systematically integrated to infer the regulatory potentials of individual genes in specific pathways. Here we report the analysis of expression patterns of genes in the Calvin cycle from 95 Arabidopsis microarray experiments, which revealed a consistent gene regulation pattern in most experiments. This identified pattern, likely due to gene regulation by light rather than feedback regulations of the metabolite fluxes in the Calvin cycle, is remarkably consistent with the rate-limiting roles of the enzymes encoded by these genes reported from both experimental and modeling approaches. Therefore, the regulatory potential of the genes in a pathway may be inferred from their expression patterns. Furthermore, gene expression analysis in the context of a known pathway helps to categorize various biological perturbations that would not be recognized with the prevailing methods.     

Effective similarity measures for expression profiles

It is commonly accepted that genes with similar expression profiles are functionally related. However, there are many ways one can measure the similarity of expression profiles, and it is not clear a priori what is the most effective one. Moreover, so far no clear distinction has been made as for the type of the functional link between genes as suggested by microarray data. Similarly expressed genes can be part of the same complex as interacting partners; they can participate in the same pathway without interacting directly; they can perform similar functions; or they can simply have similar regulatory sequences. Here we conduct a study of the notion of functional link as implied from expression data. We analyze different similarity measures of gene expression profiles and assess their usefulness and robustness in detecting biological relationships by comparing the similarity scores with results obtained from databases of interacting proteins, promoter signals and cellular pathways, as well as through sequence comparisons. We also introduce variations on similarity measures that are based on statistical analysis and better discriminate genes which are functionally nearby and faraway. Our tools can be used to assess other similarity measures for expression profiles, and are accessible at biozon.org/tools/expression/     

Preservation of ranking order in the expression of human Housekeeping genes

Housekeeping (HK) genes fulfill the basic needs for a cell to survive and function properly. Their ubiquitous expression, originally thought to be constant, can vary from tissue to tissue, but this variation remains largely uncharacterized and it could not be explained by previously identified properties of HK genes such as short gene length and high GC content. By analyzing microarray expression data for human genes, we uncovered a previously unnoted characteristic of HK gene expression, namely that the ranking order of their expression levels tends to be preserved from one tissue to another. Further analysis by tensor product decomposition and pathway stratification identified three main factors of the observed ranking preservation, namely that, compared to those of non-HK (NHK) genes, the expression levels of HK genes show a greater degree of dispersion (less overlap), stableness (a smaller variation in expression between tissues), and correlation of expression. Our results shed light on regulatory mechanisms of HK gene expression that are probably different for different HK genes or pathways, but are consistent and coordinated in different tissues.