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核受体与共调节因子信号传导中的探索性驱动研究及生物信息学

Discovery-driven research and bioinformatics in nuclear receptor and coregulator signaling.

作者信息

McKenna Neil J

机构信息

Department of Molecular and Cellular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.

出版信息

Biochim Biophys Acta. 2011 Aug;1812(8):808-17. doi: 10.1016/j.bbadis.2010.10.009. Epub 2010 Oct 26.

Abstract

Nuclear receptors (NRs) are a superfamily of ligand-regulated transcription factors that interact with coregulators and other transcription factors to direct tissue-specific programs of gene expression. Recent years have witnessed a rapid acceleration of the output of high-content data platforms in this field, generating discovery-driven datasets that have collectively described: the organization of the NR superfamily (phylogenomics); the expression patterns of NRs, coregulators and their target genes (transcriptomics); ligand- and tissue-specific functional NR and coregulator sites in DNA (cistromics); the organization of nuclear receptors and coregulators into higher order complexes (proteomics); and their downstream effects on homeostasis and metabolism (metabolomics). Significant bioinformatics challenges lie ahead both in the integration of this information into meaningful models of NR and coregulator biology, as well as in the archiving and communication of datasets to the global nuclear receptor signaling community. While holding great promise for the field, the ascendancy of discovery-driven research in this field brings with it a collective responsibility for researchers, publishers and funding agencies alike to ensure the effective archiving and management of these data. This review will discuss factors lying behind the increasing impact of discovery-driven research, examples of high-content datasets and their bioinformatic analysis, as well as a summary of currently curated web resources in this field. This article is part of a Special Issue entitled: Translating nuclear receptors from health to disease.

摘要

核受体(NRs)是一类受配体调控的转录因子超家族,它们与共调节因子及其他转录因子相互作用,以指导基因表达的组织特异性程序。近年来,该领域高内涵数据平台的产出迅速加速,产生了一系列由发现驱动的数据集,这些数据集共同描述了:核受体超家族的组织(系统发育基因组学);核受体、共调节因子及其靶基因的表达模式(转录组学);DNA中配体和组织特异性的功能性核受体及共调节因子位点(顺式作用元件组学);核受体和共调节因子形成高阶复合物的组织形式(蛋白质组学);以及它们对体内稳态和代谢的下游影响(代谢组学)。将这些信息整合到有意义的核受体和共调节因子生物学模型中,以及将数据集存档并与全球核受体信号学界进行交流,都面临着重大的生物信息学挑战。虽然该领域前景广阔,但发现驱动型研究在该领域的兴起,给研究人员、出版商和资助机构带来了共同的责任,即确保对这些数据进行有效的存档和管理。本综述将讨论发现驱动型研究影响力日益增加背后的因素、高内涵数据集及其生物信息学分析的实例,以及该领域目前策划的网络资源总结。本文是名为“将核受体从健康转化为疾病”的特刊的一部分。

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