Laboratory of Genetics and Genomics, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224-6825, USA.
Department of Systems Medicine, Mitsunada Sakaguchi Laboratory, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
BMC Bioinformatics. 2022 Sep 16;23(1):377. doi: 10.1186/s12859-022-04924-3.
Transcription factors (TFs) play central roles in maintaining "stemness" of embryonic stem (ES) cells and their differentiation into several hundreds of adult cell types. The regulatory competence of TFs is routinely assessed by detecting target genes to which they bind. However, these data do not indicate which target genes are activated, repressed, or not affected by the change of TF abundance. There is a lack of large-scale studies that compare the genome binding of TFs with the expression change of target genes after manipulation of each TF.
In this paper we associated human TFs with their target genes by two criteria: binding to genes, evaluated from published ChIP-seq data (n = 1868); and change of target gene expression shortly after induction of each TF in human ES cells. Lists of direction- and strength-specific regulated target genes are generated for 311 TFs (out of 351 TFs tested) with expected proportion of false positives less than or equal to 0.30, including 63 new TFs not present in four existing databases of target genes. Our lists of direction-specific targets for 152 TFs (80.0%) are larger that in the TRRUST database. In average, 30.9% of genes that respond greater than or equal to twofold to the induction of TFs are regulated targets. Regulated target genes indicate that the majority of TFs are either strong activators or strong repressors, whereas sets of genes that responded greater than or equal to twofold to the induction of TFs did not show strong asymmetry in the direction of expression change. The majority of human TFs (82.1%) regulated their target genes primarily via binding to enhancers. Repression of target genes is more often mediated by promoter-binding than activation of target genes. Enhancer-promoter loops are more abundant among strong activator and repressor TFs.
We developed an atlas of regulated targets of TFs (ART-TF) in human ES cells by combining data on TF binding with data on gene expression change after manipulation of individual TFs. Sets of regulated gene targets were identified with a controlled rate of false positives. This approach contributes to the understanding of biological functions of TFs and organization of gene regulatory networks. This atlas should be a valuable resource for ES cell-based regenerative medicine studies.
转录因子（TFs）在维持胚胎干细胞（ES 细胞）的“干性”及其分化为数百种成体细胞类型方面发挥着核心作用。TFs 的调控能力通常通过检测它们结合的靶基因来评估。然而，这些数据并不能表明哪些靶基因被 TF 丰度的变化激活、抑制或不受影响。缺乏大规模的研究比较 TF 的基因组结合与每个 TF 操作后靶基因表达的变化。
在本文中，我们通过两个标准将人类 TF 与其靶基因相关联：从已发表的 ChIP-seq 数据（n=1868）评估的基因结合；以及在人类 ES 细胞中诱导每个 TF 后不久靶基因表达的变化。对于 311 个 TF（在测试的 351 个 TF 中）生成了方向和强度特异性调节靶基因的列表，预期假阳性比例小于或等于 0.30，包括四个现有靶基因数据库中不存在的 63 个新 TF。我们对 152 个 TF（80.0%）的定向靶基因列表大于 TRRUST 数据库。平均而言，响应诱导 TF 大于或等于两倍的基因中，有 30.9%是调节靶基因。调节靶基因表明，大多数 TF 要么是强激活剂，要么是强抑制剂，而响应诱导 TF 大于或等于两倍的基因集在表达变化方向上没有表现出强烈的不对称性。大多数人类 TF（82.1%）主要通过结合增强子来调节其靶基因。靶基因的抑制比靶基因的激活更常通过启动子结合来介导。强激活剂和抑制剂 TF 中增强子-启动子环更为丰富。
我们通过将 TF 结合数据与单个 TF 操作后基因表达变化数据相结合，在人类 ES 细胞中开发了一个 TF 调节靶基因图谱（ART-TF）。通过控制假阳性率识别了调节基因靶标集。这种方法有助于理解 TF 的生物学功能和基因调控网络的组织。该图谱应该是基于 ES 细胞的再生医学研究的宝贵资源。
