Cell-type-specific predictive network yields novel insights into mouse embryonic stem cell self-renewal and cell fate.
Cell-type-specific predictive network yields novel insights into mouse embryonic stem cell self-renewal and cell fate.
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DOI：
10.1371/journal.pone.0056810
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Hibbs MA
中科院分区：
文献类型：
--
作者：
Dowell KG;Simons AK;Wang ZZ;Yun K;Hibbs MA
自我更新，即干细胞在保持未分化状态的同时重复分裂的能力，是所有干细胞的定义特征。在这里，我们澄清了小鼠胚胎干细胞（mESC）自我更新的分子基础，通过应用一种经过验证的贝叶斯网络机器学习方法来整合高通量数据，用于蛋白质功能发现。通过专注于单个干细胞系统，在特定的发育阶段，在已知在该细胞类型中活跃的明确定义的生物过程的背景下，我们产生了一个共识预测网络，该网络比先前使用更普遍，上下文无关的方法所做的努力更接近地反映了生物现实。此外，我们还展示了如果哺乳动物蛋白质的组织特异性作用在训练集中没有定义，并且在证据数据中没有限制，机器学习的努力可能会被误导。在这项研究中，我们收集了大量的mESC数据：在992种条件下，从60项不同的研究中收集了220万个数据点。然后，我们将这些数据整合到一个共识的mESC功能关系网络集中在与胚胎干细胞自我更新和细胞命运决定相关的生物学过程。计算评估，文献验证和预测的功能链接的分析表明，我们的结果是高度准确的和生物相关的。我们的mESC网络预测了许多参与自我更新的新参与者，并为未来的多能干细胞研究奠定了基础。这个网络可以被干细胞研究人员（在http://StemSight.org）用来探索关于自我更新背景下基因功能的假设，并优先考虑实验验证的感兴趣的基因。
Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC) self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org) to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation.
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影响因子：
4.3
作者：
Guan Y;Myers CL;Lu R;Lemischka IR;Bult CJ;Troyanskaya OG
通讯作者：
Troyanskaya OG
指导实验生物学：线粒体生物发生的案例研究。
影响因子：
4.3
作者：
Hibbs MA;Myers CL;Huttenhower C;Hess DC;Li K;Caudy AA;Troyanskaya OG
通讯作者：
Troyanskaya OG
影响因子：
5.8
作者：
Huttenhower, Curtis;Schroeder, Mark;Troyanskaya, Olga G.
通讯作者：
Troyanskaya, Olga G.
影响因子：
5.8
作者：
Hibbs, Matthew A.;Hess, David C.;Troyanskaya, Olga G.
通讯作者：
Troyanskaya, Olga G.
影响因子：
4.3
作者：
Gillis J;Pavlidis P
通讯作者：
Pavlidis P