Interaction of cellular-localized signature modules in response to prostate cancer

Rapid progress in high-throughput biotechnologies (e.g. microarrays) and exponential accumulation of gene functional knowledge makes it promising for systematic understanding of complex human diseases at the functional modules level. Current modular categorizations can be defined and selected more specifically and precisely in terms of both biological processes and cellular locations, aiming at uncovering the modular molecular networks highly relevant to cancers. Based on Gene Ontology, we identifed the functional modules enriched with differentially expressed genes and characterized by biological processes and specific cellular locations. Then, according to the ranking of the disease discriminating abilities of the pre-selected functional modules, we further defined and filtered signature modules which have higher relevance to the cancer under study. Applications of the proposed method to the analysis of a prostate cancer dataset revealed insightful biological modules.     

Interaction of cellular-localized signature modules in response to prostate cancer

Rapid progress in high-throughput biotechnologies (e.g. microarrays) and exponential accumulation of gene functional knowledge makes it promising for systematic understanding of complex human diseases at the functional modules level. Current modular categorizations can be defined and selected more specifically and precisely in terms of both biological processes and cellular locations, aiming at uncovering the modular molecular networks highly relevant to cancers. Based on Gene Ontology, we identifed the functional modules enriched with differentially expressed genes and characterized by biological processes and specific cellular locations. Then, according to the ranking of the disease discriminating abilities of the pre-selected functional modules, we further defined and filtered signature modules which have higher relevance to the cancer under study. Applications of the proposed method to the analysis of a prostate cancer dataset revealed insightful biological modules.     

Identifying disease feature genes based on cellular localized gene functional modules and regulation networks

Using data mining methods to identify disease-rele- vant genes and functional modules via high throughput gene expression profiles is of critical importance for studying complex disease mechanisms and predicting disease phenotypes[1,2]. Genes do not behav…     

基于蛋白质相互作用网络的蛋白质复合物和功能模块预测算法研究进展

蛋白质相互作用网络中的模块化结构通常对应于蛋白质复合物或者蛋白质功能模块。基于蛋白质相互作用网络预测蛋白质复合物和功能模块不仅有助于理解生命有机体的细胞生物过程,而且可为探讨疾病的发生、发展和治疗以及合理的药物开发提供重要的基础。本文通过回顾近二十年来基于蛋白质相互作用网络的蛋白质复合物和功能模块预测算法研究的发展历程,按照静态蛋白质相互作用网络(SPIN)和动态蛋白质相互作用网络(DPIN)两个方向分别梳理预测算法所涉及的方法和技术,同时归纳常用的数据集并分析所面临的问题,为进一步研究提供有价值的参考。     

基于功能模块的基因表达谱聚类分析

按Gone Ontology基因功能分类体系，将基因模块化地组织成具有显著生物意义的低维功能模块单元，并将其作为新的分析指标用于分类微阵列疾病样本，从而提出了基于功能表达谱的聚类分析新途径、采用NCI60数据集，通过功能表达谱对组织样本进行聚类分析．结果显示，新算法不但得到高准确度的样本分型结果，而且能够直接从功能水平上给出相应的生物学解释．同时，用基于功能表达谱对组织样本进行聚类分析可以显著降低特征维数，有效地处理高检测误差与基因表达变异问题．     

Functional cartography of complex metabolic networks

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a 'cartographic representation' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.     

浅谈利用最新TTS技术进行语音图书馆建设

介绍了利用最新语音合成TTS技术,通过文本数据挖掘、人工语音文件生成和音频数据压缩三部分的功能模块,把图书馆馆藏资源制作成有声资源的技术要领。     

Functional dissection of lysine deacetylases reveals that HDAC1 and p300 regulate AMPK

First identified as histone-modifying proteins, lysine acetyltransferases (KATs) and deacetylases (KDACs) antagonize each other through modification of the side chains of lysine residues in histone proteins. Acetylation of many non-histone proteins involved in chromatin, metabolism or cytoskeleton regulation were further identified in eukaryotic organisms, but the corresponding enzymes and substrate-specific functions of the modifications are unclear. Moreover, mechanisms underlying functional specificity of individual KDACs remain enigmatic, and the substrate spectra of each KDAC lack comprehensive definition. Here we dissect the functional specificity of 12 critical human KDACs using a genome-wide synthetic lethality screen in cultured human cells. The genetic interaction profiles revealed enzyme-substrate relationships between individual KDACs and many important substrates governing a wide array of biological processes including metabolism, development and cell cycle progression. We further confirmed that acetylation and deacetylation of the catalytic subunit of the adenosine monophosphate-activated protein kinase (AMPK), a critical cellular energy-sensing protein kinase complex, is controlled by the opposing catalytic activities of HDAC1 and p300. Deacetylation of AMPK enhances physical interaction with the upstream kinase LKB1, leading to AMPK phosphorylation and activation, and resulting in lipid breakdown in human liver cells. These findings provide new insights into previously underappreciated metabolic regulatory roles of HDAC1 in coordinating nutrient availability and cellular responses upstream of AMPK, and demonstrate the importance of high-throughput genetic interaction profiling to elucidate functional specificity and critical substrates of individual human KDACs potentially valuable for therapeutic applications.     

基于大脑不同区域的阿尔茨海默症基因表达数据分析

提出了采用Tukey双权函数作为FastICA(Fast Independent Component Analysis)方法的非线性函数,对阿尔茨海默症(Alzheimer’s disease, AD)多个脑区域基因表达数据进行显著基因提取,揭示其基因表达调控关系.针对传统聚类方法基于全局聚类且只能将某个基因聚类到某一类的缺陷,改进的FastICA方法能够对基因表达数据进行快速有效的双向聚类,能够满足同一个基因可能参与不同信号传导通路的生物特性.同时考虑到人脑中海马区、内嗅皮质区、颞中回及视觉皮层区均与学习与记忆功能密切相关,将算法对多个脑区域进行基因表达调控综合分析.结果表明,大量炎症反应是AD致病的重要因素之一.     

Proteome survey reveals modularity of the yeast cell machinery

Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. Here we report the first genome-wide screen for complexes in an organism, budding yeast, using affinity purification and mass spectrometry. Through systematic tagging of open reading frames (ORFs), the majority of complexes were purified several times, suggesting screen saturation. The richness of the data set enabled a de novo characterization of the composition and organization of the cellular machinery. The ensemble of cellular proteins partitions into 491 complexes, of which 257 are novel, that differentially combine with additional attachment proteins or protein modules to enable a diversification of potential functions. Support for this modular organization of the proteome comes from integration with available data on expression, localization, function, evolutionary conservation, protein structure and binary interactions. This study provides the largest collection of physically determined eukaryotic cellular machines so far and a platform for biological data integration and modelling.     

Probing cellular protein complexes using single-molecule pull-down

Proteins perform most cellular functions in macromolecular complexes. The same protein often participates in different complexes to exhibit diverse functionality. Current ensemble approaches of identifying cellular protein interactions cannot reveal physiological permutations of these interactions. Here we describe a single-molecule pull-down (SiMPull) assay that combines the principles of a conventional pull-down assay with single-molecule fluorescence microscopy and enables direct visualization of individual cellular protein complexes. SiMPull can reveal how many proteins and of which kinds are present in the in vivo complex, as we show using protein kinase A. We then demonstrate a wide applicability to various signalling proteins found in the cytosol, membrane and cellular organelles, and to endogenous protein complexes from animal tissue extracts. The pulled-down proteins are functional and are used, without further processing, for single-molecule biochemical studies. SiMPull should provide a rapid, sensitive and robust platform for analysing protein assemblies in biological pathways.     

Research progress in quantifying the mechanical properties of single living cells using atomic force microscopy

The advent of atomic force microscopy （AFM） provides a powerful tool for investigating the behaviors of single living cells under near physiological conditions. Besides acquiring the images of cellular ultra-microstruc- tures with nanometer resolution, the most remarkable advances are achieved on the use of AFM indenting tech- nique to quantify the mechanical properties of single living ceils. By indenting single living cells with AFM tip, we can obtain the mechanical properties of cells and monitor their dynamic changes during the biological processes （e.g., after the stimulation of drugs）. AFM indentation-based mechan- ical analysis of single cells provides a novel approach to characterize the behaviors of cells from the perspective of biomechanics, considerably complementing the traditional biological experimental methods. Now, AFM indentation technique has been widely used in the life sciences, yielding a large amount of novel information that is meaningful to our understanding of the underlying mechanisms that govern the cellular biological functions. Here, based on the authors＇ own researches on AFM measurement of cellular mechani- cal properties, the principle and method of AFM indentationtechnique was presented, the recent progress of measuring the cellular mechanical properties using AFM was summa- rized, and the challenges of AFM single-cell nanomechani- cal analysis were discussed.     

基于网络药理学的金荞麦治疗糖尿病肺病的作用机制

基于网络药理学研究金荞麦治疗糖尿病肺病的主要化学成分,作用靶点和相关信号通路.利用TCMSP数据库检索金荞麦主要的活性成分及作用靶点;基于OMIM和Gene Cards数据库找出与糖尿病肺病相关的作用靶点,求金荞麦主要成分的作用靶点与糖尿病肺病的相关靶点交集,将交集靶点导入STRING数据库构建PPI网络,对该网络进行拓扑分析找出关键靶点;把关键靶点导入DAVID 6.8.0数据库,找出金荞麦治疗糖尿病肺病关键靶点参与的生物学过程、分子功能、细胞组分和KEGG通路.结果显示,通过TCMSP数据库的口服利用度(OB)和类药性(DL)条件筛选出15个金荞麦主要化合物,相应作用靶点85个;与糖尿病肺病发病机制有关的作用靶点18个,经PPI网络分析得到9个关键靶点;GO分析富集得到生物过程72个,分子功能14个,细胞组分8个,65条KEGG信号通路.表明金荞麦治疗糖尿病肺病发挥药效的有效成分可能是没食子酸酯、(+)-儿茶素、β-谷甾醇、异鼠李素和原花青素B1等化合物,作用机制可能跟MAPK1、AKT1、EGFR、EGF、JUN等靶点,以及Erb B信号通路、Ras信号通路、癌症中的胆碱代谢、MAPK信号通路、局灶性粘连及TNF信号通路有关.     

脑肌电多设备集成系统的接口硬件设计

脑肌电控制是使用人的脑电和肌电信号对设备进行控制的新型人机控制方式。为了方便其多设备类型集成控制系统的构建,设计了一种兼容多设备控制信号的新型脑肌电控制系统信号接口,详细描述了其3层结构工作原理、不同模式的工作流程以及各功能模块的硬件设计。接口还具备针对误操作的安全保护功能,该功能采用显示反馈和二次确认的方式实现,可以有效优化控制系统的安全性能。     

基于Pro/E平台的CAD/CAPP/CAM系统集成化研究

针对目前Pro/E软件的应用大多数集中在其CAD功能上,而Pro/E的CAM功能未能得到充分的应用以及CAD功能的应用中很多设计过程存在重复劳动的问题,研究了在Pro/E系统下运用Pro/E的二次开发工具Pro/TOOLKIT实现CAD/CAPP/CAM集成的一般方法,并利用动态链接库(DLL)方式实现了使用VC 6.0的MFC类库对应用程序用户界面的开发.从系统总体设计及各功能模块的构建等方面给出了详细的设计方案,实现了相关过程的自动处理,提高了Pro/E系统的实际应用程度.     

Human metabolic individuality in biomedical and pharmaceutical research

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.     

Functional genomic analysis of cell division in C. elegans using RNAi of genes on chromosome III

Genome sequencing projects generate a wealth of information; however, the ultimate goal of such projects is to accelerate the identification of the biological function of genes. This creates a need for comprehensive studies to fill the gap between sequence and function. Here we report the results of a functional genomic screen to identify genes required for cell division in Caenorhabditis elegans. We inhibited the expression of approximately 96% of the approximately 2,300 predicted open reading frames on chromosome III using RNA-mediated interference (RNAi). By using an in vivo time-lapse differential interference contrast microscopy assay, we identified 133 genes (approximately 6%) necessary for distinct cellular processes in early embryos. Our results indicate that these genes represent most of the genes on chromosome III that are required for proper cell division in C. elegans embryos. The complete data set, including sample time-lapse recordings, has been deposited in an open access database. We found that approximately 47% of the genes associated with a differential interference contrast phenotype have clear orthologues in other eukaryotes, indicating that this screen provides putative gene functions for other species as well.     

Substance graphs are optimal simple-graph representations of metabolism

One approach to study the system-wide organization of biochemistry is to use statistical graph theory. In such heavily simplified methods, which disregard most of the dynamic aspects of biochemistry, one is faced with fundamental questions. One such question is how the chemical reaction systems should be reduced to a graph retaining as much functional information as possible from the original reaction system. In these graph representations, should the edges go between substrates and products, or substrates and substrates, or both? Should vertices represent substances or reactions? Different representations encode different information about the reaction system and affect network measures in different ways. This paper investigates which representation reflects the functional organization of the metabolic system in the best way, according to the defined criteria. Four different graph representations of metabolism (three where the vertices are metabolites, one where the vertices are reactions) are evaluated using data from different organisms and databases. The graph representations are evaluated by comparing the overlap between clusters (network modules) and annotated functions, and also by comparing the set of identified currency metabolites with those that other authors have identified using qualitative biological arguments. It is found that a “substance network”, where all metabolites participating in a reaction are connected, is better than others, evaluated with respect to both the functional overlap between modules and functions and to the number and identity of the identified currency metabolites.