Altered chromatin landscape in circulating T follicular helper and regulatory cells following grass pollen subcutaneous and sublingual immunotherapy

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Tissue-specific functions based on information content of gene ontology using cap analysis gene expression

Gene expressions differ depending on tissue types and developmental stages. Analyzing how each gene is expressed is thus important. One way of analyzing gene expression patterns is to identify tissue-specific functions. This is useful for understanding how vital activities are performed. DNA microarray has been widely used to observe gene expressions exhaustively. However, comparing the expression value of a gene to that of other genes is impossible, as the gene expression value of a condition is measured as a proportion of that for the same gene under a control condition. We therefore could not determine whether one gene is more expressed than other genes. Cap analysis gene expression (CAGE) allows high-throughput analysis of gene expressions by counting the number of cDNAs of expressed genes. CAGE enables comparison of the expression value of the gene to that of other genes in the same tissue. In this study, we propose a method for exploring tissue-specific functions using data from CAGE. To identify tissue-specificity, one of the simplest ways is to assume that the function of the most expressed gene is regarded as the most tissue-specific. However, the most expressed gene in a tissue might highly express in all tissues, as seen with housekeeping genes. Functions of such genes cannot be tissue-specific. To remove these from consideration, we propose measuring tissue specificity of functions based on information content of gene ontology terms. We applied our method to data from 16 human tissues and 22 mouse tissues. The results from liver and prostate gland indicated that well-known functions of these tissues, such as functions related to signaling and muscle in prostate gland and immune function in liver, displayed high rank.     

Nasopharyngeal Carcinoma Protein Interaction Mapping Analysis via Proteomic Approaches

Nasopharyngeal carcinoma (NPC), although not very common in many parts of the world, is a major concern insome countries, including Iran. Molecular studies are very helpful to provide essential information regarding underlyingcarcinogenetic mechanisms. Here, considering NPC proteomic approaches, established biomarkers were designated forprotein-protein interaction network construction and analysis with corresponding plug-ins. A network of reported proteinmarkers was constructed and topological and biological process features were investigated. Centrality analysis showedthat JUN, CALM1, HSB1, and SOD1 are more important than other differentially expressed proteins in an interactingpattern. What is more, by extending the network, Tp53, PRDM10, AKT1, ALB, HSP90AA1, and EGFR achieved thehighest values for NPC network strength. It can be concluded that these proteins as well as their contributing processes,particularly in a second network, may be important for NPC onset and development. Targeting these candidate proteinsmay allow novel treatment approaches following appropriate validation.     

A genome-wide nanotoxicology screen of Saccharomyces cerevisiae mutants reveals the basis for cadmium sulphide quantum dot tolerance and sensitivity

The use of cadmium sulphide quantum dots (CdS QDs) is increasing, particularly in the electronics industry. Their size (1–10?nm in diameter) is, however, such that they can be taken up by living cells. Here, a bakers’ yeast (Saccharomyces cerevisiae) deletion mutant collection has been exploited to provide a high-throughput means of revealing the genetic basis for tolerance/susceptibility to CdS QD exposure. The deletion of 112 genes, some associated with the abiotic stress response, some with various metabolic processes, some with mitochondrial organization, some with transport and some with DNA repair, reduced the level of tolerance to CdS QDs. A gene ontology analysis highlighted the role of oxidative stress in determining the cellular response. The transformation of sensitive mutants with centromeric plasmids harbouring DNA from a wild type strain restored the wild type growth phenotype when the complemented genes encoded either HSC82, DSK2 or ALD3. The use of these simple eukaryote knock-out mutants for functional toxicogenomic analysis will inform studies focusing on higher organisms.     

基于系统药理学探索甘草有效成分甘草甜素的药理作用机制

该文拟通过一系列系统药理学方法探讨甘草甜素的药理作用机制。运用文本挖掘工具,获得甘草甜素主要相关疾病;在Polysearch和PubChem 数据库获得甘草甜素的靶蛋白;运用网络可视化软件,构建甘草甜素靶蛋白质相互作用网络;借助基因本位论 (GO) 工具分析甘草甜素蛋白质群;通路富集分析甘草甜素靶蛋白的作用通路。文本挖掘结果表明甘草甜素主要相关疾病有慢性丙型肝炎、慢性肝炎、肝炎、HIV病毒、肝癌等;基因本位论分析结果表明甘草甜素主要通过修饰蛋白、修饰染色质等作用发挥生物学功能;信号通路富集结果表明甘草甜素主要作用蛋白与MAPK信号通路、Toll样受体信号通路、神经营养因子信号通路、癌症途径以及细胞凋亡等信号通路相关。可见,甘草甜素可能主要通过调控MAPK信号通路、Toll样受体信号通路等多途径发挥其药理作用。综合运用系统药理学的方法可以实现快速而全面的分析药物的药理作用。     

儒家“独立守中”思想及对中医诊疗的指导意义

从学术体系与历史发展的角度分析中医学与儒家的关系。基于儒家经典阐述的思想体系,从本体、变动、显效三个维度,深入分析了儒家"独立守中"思想本质,提出了儒家思想一元论的原则和方法,并从本体角度分析了儒家"独立守中"思想与中医学的同一性,进一步探讨了其在中医诊疗过程中不可或缺的指导意义。     

Novel approaches to develop community-built biological network models for potential drug discovery

Introduction: Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data.

Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described.

Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients’ differences with respect to disease prevention, diagnosis, and therapy outcome.