结核分枝杆菌感染对人巨噬细胞离子通道及其调控元件转录表达的影响

为研究人巨噬细胞的离子通道及其调控元件是否参与了抗结核分枝杆菌感染免疫,利用表达谱芯片技术研究细菌感染后主巨噬细胞基因的表达情况,在全局表达谱分析的基础上,重点分析了离子通道及其调控元件的表达,并比较无毒株和临床分离有毒株在诱导离子通道及其调控元件表达方面的差异。结果表明,细菌感染影响离子通道及其调控元件基因的表达,涉及的离子通道包括钾通道、钠通道、氯通道、钙通道,差异表达的调控元件包括G蛋白、G蛋白偶联受体、蛋白质激酶和磷酸化酶;临床株感染影响的离子通道及其调控元件较无毒株广泛和丰富。这些观察结果提示,离子通道及其调控元件参与了宿主细胞对感染细菌的免疫应答,有关离子通道及其调控元件在抗结核免疫中的作用有待进一步研究。芯片研究的结果为将来的研究提供了线索。     

电压门控离子通道功能与结构分子模型

离子通道是细胞膜上特殊跨膜蛋白构成的亲水孔道,越来越多的证据表明其与兴奋性,腺体分泌、机体运动、甚至学习和记忆行为等重要生理现象密切相关,由此该领域成为当今年生命学科广为注目的前沿之一。本将简要介绍离子通道的分类和功能,并侧重阐明通道压控原理有压控通道的跨膜拓扑结构和功能分子模型。     

DICE：电压门控离子通道电生理实验数据库

针对现有相关文献中离子通道电生理数据繁多且分散的特点,开发了一套电压门控离子通道电生理实验数据库。数据库中目前主要包括钠离子通道序列数据、调制剂分子结构和序列数据,并收集整理了文献中调制剂和通道相互作用时的电生理学数据和药理学数据。系统实现了数据的收集、录入、存储和查询,为后期进行数据挖掘奠定了基础。用户可以通过网址http：／／biodb．sgst．cn／DICE对数据库进行访问。     

离子通道与人类遗传病

细胞膜离子通道结构和功能正常是细胞进行生理活动的基础,对离子通道功能具有决定性意义的特定位点的突变导致其开放、关闭或激活、失活功能异常,引起组织机能紊乱,形成各种遗传性疾病。本文从水通道蛋白,钙通道,钠通道,钾通道等多种通道蛋白引起的遗传病的现象以及机理做较深入的阐述。     

一种基于类均值的肿瘤基因芯片数据的标准化方法

：分析了当前常用的标准化方法在肿瘤基因芯片中引起错误分类的原因,提出了一种基于类均值的标准化方法．该方法对基因表达谱进行双向标准化,并将标准化过程与聚类过程相互缠绕,利用聚类结果来修正参照表达水平．选取了5组肿瘤基因芯片数据,用层次聚类和K-均值聚类算法在不同的方差水平上分别对常用的标准化和基于类均值的标准化处理后的基因表达数据进行聚类分析比较．实验结果表明,基于类均值的标准化方法能有效提高肿瘤基因表达谱聚类结果的质量．     

结合基因功能分类体系Gene Ontology筛选聚类特征基因

使用两套基因表达谱数据,按各基因的表达值方差,选择表达变异基因对样本聚类,发现一般使用方差较大的前10％的基因作为特征基因,就可以较好地对疾病样本聚类。对不同的疾病,包含聚类信息的特征基因有不同的分布特点。在此基础上,结合基因功能分类体系(Gene Ontology,GO),进一步筛选聚类的特征基因。通过检验在Gene Ontology中的每个功能类中的表达变异基因是否非随机地聚集,寻找疾病相关功能类,再根据相关功能类中的表达变异基因进行聚类分析。实验结果显示：结合基因功能体系进一步筛选表达变异基因作为聚类特征基因,可以保持或提高聚类准确性,并使得聚类结果具有明确的生物学意义。另外,发现了一些可能和淋巴瘤和白血病相关的基因。     

T淋巴细胞上的离子通道

近年的研究证明,淋巴细胞上的离子通道,在免疫功能调节中具有重要的作用。T淋巴细胞上主要有三类离子通道,即Ca2 、K 和C1-通道。Ca2 通过T淋巴细胞膜上的Ca2 通道(CRAC)进入细胞内,可作为第二信使激活T淋巴细胞。通过K 通道的K 外流是T淋巴细胞膜电位形成的基础。由于膜电位水平可以影响钙离子的内流,因此,K 通道可以间接调节T淋巴细胞的活化和功能。T淋巴细胞上的Cl-通道是新近发现的一种离子通道,可能与细胞的体积调节有关。本文扼要总结了T淋巴细胞上离子通道的新近进展。     

脆性X综合征FMRP缺失对离子通道的异常调控

脆性X综合征(FXS)由脆性X智力低下蛋白FMRP表达降低甚至完全缺失引起,是最常见的遗传性智力缺陷综合征和孤独症谱系障碍的单基因致病因素。FMRP不仅可与离子通道mRNA结合,如电压门控钾通道(Kv3.1和Kv4.2)等,还直接与多个离子通道作用,如钠激活钾通道(Slack)等。FMRP的缺失导致神经元离子通道表达异常和功能失调,在不同的脑区和不同的神经细胞类型中引起特定的离子稳态失衡、膜电位改变和兴奋性失常,导致神经环路过度兴奋。现就FMRP缺失对不同离子通道的异常调控及其研究进展进行综述。     

一种从多表达谱数据挖掘基因共表达团的新方法

随着近年来高通量基因表达谱数据的涌现,集成多个不同实验条件的表达谱数据,并挖掘在多数据源都保守的基因共表达团,成为预测基因功能或者调控关系的方法之一．但是,常用的方法通常仅简单地集成不同表达谱数据并推导保守基因共表达团,这样可能会导致结果中出现并非真正在多数据源保守的共表达团．提出一种结合最小哈希与局部敏感哈希的新方法,可以高效地寻找在多表达谱数据源中真正保守的基因共表达团．结果分析证明,相比过去的方法,现提出的方法可以获得更加功能相关和调控相关的基因共表达团．     

昆虫中枢DUM神经元受体和离子通道电生理学研究进展

背侧不成对中间神经元(DUM)是一类位于多种昆虫腹神经索神经节背侧的神经元,能自发产生内源性超射动作电位。在DUM神经元细胞膜表达多种受体和离子通道,且电生理特性有别于哺乳动物中枢神经元膜上同种类型的受体和离子通道。目前已证实其细胞膜上表达K+通道、电压依赖的Na+通道、Ca2+敏感的Cl-通道、Ca2+通道、氯离子通道、乙酰胆碱受体、谷氨酸受体等多种离子通道和受体。近年来因膜片钳(patch-clamp)技术进展和对受体和离子通道研究的深入,该类神经细胞已用于杀虫剂选择性神经毒性研究和杀虫剂离体筛选。     

Ion Channel Gene Expression in Lung Adenocarcinoma: Potential Role in Prognosis and Diagnosis

Ion channels are known to regulate cancer processes at all stages. The roles of ion channels in cancer pathology are extremely diverse. We systematically analyzed the expression patterns of ion channel genes in lung adenocarcinoma. First, we compared the expression of ion channel genes between normal and tumor tissues in patients with lung adenocarcinoma. Thirty-seven ion channel genes were identified as being differentially expressed between the two groups. Next, we investigated the prognostic power of ion channel genes in lung adenocarcinoma. We assigned a risk score to each lung adenocarcinoma patient based on the expression of the differentially expressed ion channel genes. We demonstrated that the risk score effectively predicted overall survival and recurrence-free survival in lung adenocarcinoma. We also found that the risk scores for ever-smokers were higher than those for never-smokers. Multivariate analysis indicated that the risk score was a significant prognostic factor for survival, which is independent of patient age, gender, stage, smoking history, Myc level, and EGFR/KRAS/ALK gene mutation status. Finally, we investigated the difference in ion channel gene expression between the two major subtypes of non-small cell lung cancer: adenocarcinoma and squamous-cell carcinoma. Thirty ion channel genes were identified as being differentially expressed between the two groups. We suggest that ion channel gene expression can be used to improve the subtype classification in non-small cell lung cancer at the molecular level. The findings in this study have been validated in several independent lung cancer cohorts.     

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge

Method

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping.

Results

I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.     

Empirical evidence of the applicability of functional clustering through gene expression classification

The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact, and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using 10 benchmark data sets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed.     

A test of the coordinated expression hypothesis for the origin and maintenance of the GAL cluster in yeast

Metabolic gene clusters--functionally related and physically clustered genes--are a common feature of some eukaryotic genomes. Two hypotheses have been advanced to explain the origin and maintenance of metabolic gene clusters: coordinated gene expression and genetic linkage. Here we test the hypothesis that selection for coordinated gene expression underlies the clustering of GAL genes in the yeast genome. We find that, although clustering coordinates the expression of GAL1 and GAL10, disrupting the GAL cluster does not impair fitness, suggesting that other mechanisms, such as genetic linkage, drive the origin and maintenance metabolic gene clusters.