A Technique for Measuring Transcription Factor Activity

摘要 几乎NIH的每个部门都在了解转录因子(TF)方面投入了大量资金。TF是 细胞的管理者，控制着从细胞类型到细胞反应到压力的一切。凭借其强大的力量， 难怪，许多人类疾病(癌症、家族性血小板紊乱、瓦登堡综合征等)结果 来自转录因子的突变。此外，超过75%的疾病导致人类体内的变异 基因组驻留在调控区，那里有密集的TF结合位点。目前我们可以测量 Tf结合的位置，但结合并不等同于调节活性。此外，绑定分析是 一次指挥一个特遣部队。人们迫切需要的是一种能够测量人类活动的技术 同时在一个单元格中的所有TF。我们已经开发了一种新的方法，称为Erna分析，它利用 增强子RNA利用所有eRNA的位置同时推断细胞中所有TF的活性。这个 这个多样性补充延期请求的主要目标是在体内评估我们的计算极限 建立预测因子活性的模型。通过这种方式，我们寻求优化我们的技术，使Erna 仿形更准确、更快速、适用范围更广。

Summary Almost every division of NIH has invested heavily in understanding transcription factors (TFs). TFs are the managers of the cell, controlling everything from cell type to cellular response to stress. With their great power, it is no wonder, many human disorders (cancer, familial platelet disorder, Waardenburg syndrome, etc.) result from mutations in transcription factors. Moreover, over 75% of disease causing variants within the human genome reside in regulatory regions, which are dense with TF binding sites. Currently we can measure the location of TF binding, but binding does not equate with regulatory activity. Furthermore, binding analysis is conducted one TF at a time. What is desperately needed is a technology that is able to measure the activity of all TFs in a cell simultaneously. We have developed a novel approach, called eRNA profiling, that leverages enhancer RNAs to infer the activity of all TFs in a cell simultaneously using the location of all eRNAs. The primary goal of this diversity supplement extension request is to evaluate in vivo the limits of our computational modeling towards predicting the activity of TFs. In this way, we seek to optimize our technology, making eRNA profiling more accurate, fast and broadly applicable.