E Pluribus Unum: Understanding and Influencing Pluripotency & Reprogramming by Integrative Bioinformatics

E Pluribus Unum：理解和影响多能性

• 批准号: 66373205
• 负责人: Professor Dr. Georg Fuellen
• 金额: --
• 依托单位: Institut für Biostatistik und Informatik in Medizin und Alternsforschung
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/66373205?language=en
• 关键词: Pluribus; Unum; Understanding; Influencing; Pluripotency

Following up on work accomplished in the past 20 months since SPP funding commenced, we intend to complete machine learning of pluripotency, now focusing on data mining & integration, based on our PluriNetWork, and associated ExprEssence software that suggests mechanisms involved in pluripotency, by comparing high-throughput datasets. We will expand the PluriNetWork of pluripotency genes/proteins in mouse, and establish it for human (& other species), focusing on transcriptional regulation, signaling & epigenetics. Understanding pluripotency & reprogramming is then based on integrating differential highthroughput datasets into our networks and identifying putative mechanisms, comparing a) different species, b) different data types (protein, RNA, epigenetics, …), and c) different interventions & time series, different states of pluripotency (ICM, Epiblast, iPS, …) & different reprogramming approaches (iPS, SCNT, fusion). We dissect pluripotency & reprogramming in an analysis across all dimensions, using the networks to integrate the data, and to propose intervention schemes (integrating small molecules). The differential analyses by ExprEssence are developed further to cope with these new tasks, incl. components for generating movies, highlighting the startup & shutdown of mechanisms in a network along all dimensions. ExprEssence highlighting will thus identify ensembles of subnetworks to be subjected to detailed modeling (Theis), it will support Boiani, Greber, Meisterernst, Hiiragi, Schroeder, Tanaka, Besser, F.-J. Müller, A. Müller & Zenke, and it is also the future core of the Bioinformatics Helpdesk for the SPP.

在SPP资助开始以来的过去20个月完成的工作之后，我们打算完成多能性的机器学习，现在专注于数据挖掘和集成，基于我们的PluriNetWork和相关的ExprEssence软件，通过比较高通量数据集，提出多能性所涉及的机制。我们将扩展小鼠多能性基因/蛋白的PluriNetWork，并建立人类（和其他物种），专注于转录调控，信号传导和表观遗传学。理解多能性和重编程是基于将差异高通量数据集整合到我们的网络中并识别推定的机制，比较a）不同物种，B）不同数据类型（蛋白质，RNA，表观遗传学，.），c）不同干预和时间序列，多能性的不同状态（ICM，外胚层，iPS，.）和不同的重编程方法（iPS，SCNT，融合）。我们在所有维度的分析中剖析了多能性和重编程，使用网络来整合数据，并提出干预方案（整合小分子）。ExprEssence的差分分析进一步发展，以科普这些新的任务，包括。用于生成电影的组件，突出显示网络中沿着所有维度的机制的启动和关闭。因此，ExprEssence突出显示将识别要进行详细建模的子网络的集合（Theis），它将支持Boiani，Greber，Meisterernst，Hiiragi，Schroeder，Tanaka，Besser，F. J. Müller，A. Müller & Zenke，它也是SPP生物信息学帮助台的未来核心。

项目成果

Bioinformatics approaches to single-blastomere transcriptomics.

• DOI: 10.1093/molehr/gau083
• 发表时间: 2015-02
• 期刊: Molecular human reproduction
• 影响因子: 4
• 作者: L. Taher;M. Pfeiffer;G. Fuellen

Derivation of an interaction/regulation network describing pluripotency in human.

描述人类多能性的相互作用/调节网络的推导

• DOI: 10.1016/j.gene.2012.04.025
• 发表时间: 2012
• 期刊: Gene
• 影响因子: 3.5
• 作者: Lustrek M;Singh NK;Fuellen G
• 通讯作者: Fuellen G

Mitochondrial physiology and gene expression analyses reveal metabolic and translational dysregulation in oocyte-induced somatic nuclear reprogramming.

• DOI: 10.1371/journal.pone.0036850
• 发表时间: 2012
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Esteves TC;Psathaki OE;Pfeiffer MJ;Balbach ST;Zeuschner D;Shitara H;Yonekawa H;Siatkowski M;Fuellen G;Boiani M
• 通讯作者: Boiani M