School of Electrical Engineering and Compute Science, University of Ottawa, 800 King Edward Ave., Ottawa, K1N 6N5, Ontario, Canada.
Regenerative Medicine Program, Ottawa Hospital Research Institute, 501 Smyth Rd., Ottawa, K1H 8L6, Ontario, Canada.
BMC Genomics. 2024 Oct 14;25(1):957. doi: 10.1186/s12864-024-10859-1.
Transcription factors (TFs) bind to different parts of the genome in different types of cells, but it is usually assumed that the inherent DNA-binding preferences of a TF are invariant to cell type. Yet, there are several known examples of TFs that switch their DNA-binding preferences in different cell types, and yet more examples of other mechanisms, such as steric hindrance or cooperative binding, that may result in a "DNA signature" of differential binding.
To survey this phenomenon systematically, we developed a deep learning method we call SigTFB (Signatures of TF Binding) to detect and quantify cell-type specificity in a TF's known genomic binding sites. We used ENCODE ChIP-seq data to conduct a wide scale investigation of 169 distinct TFs in up to 14 distinct cell types. SigTFB detected statistically significant DNA binding signatures in approximately two-thirds of TFs, far more than might have been expected from the relatively sparse evidence in prior literature. We found that the presence or absence of a cell-type specific DNA binding signature is distinct from, and indeed largely uncorrelated to, the degree of overlap between ChIP-seq peaks in different cell types, and tended to arise by two mechanisms: using established motifs in different frequencies, and by selective inclusion of motifs for distint TFs.
While recent results have highlighted cell state features such as chromatin accessibility and gene expression in predicting TF binding, our results emphasize that, for some TFs, the DNA sequences of the binding sites contain substantial cell-type specific motifs.
转录因子 (TFs) 在不同类型的细胞中结合到基因组的不同部位，但通常假定 TF 的固有 DNA 结合偏好对于细胞类型是不变的。然而，有几个已知的例子表明 TFs 在不同的细胞类型中改变了它们的 DNA 结合偏好，还有更多其他机制的例子，如空间位阻或协同结合，可能导致差异结合的“DNA 特征”。
为了系统地调查这种现象，我们开发了一种深度学习方法，称为 SigTFB（转录因子结合的特征），用于检测和量化 TF 在其已知基因组结合位点中的细胞类型特异性。我们使用 ENCODE ChIP-seq 数据对多达 14 种不同细胞类型中的 169 种不同的 TF 进行了广泛的研究。SigTFB 在大约三分之二的 TF 中检测到具有统计学意义的 DNA 结合特征，远远超过之前文献中相对较少的证据所预期的数量。我们发现，存在或不存在细胞类型特异性 DNA 结合特征与 ChIP-seq 峰在不同细胞类型之间的重叠程度不同，而且实际上是不相关的，并且倾向于通过两种机制出现：以不同的频率使用已建立的基序，以及选择性地包含不同 TF 的基序。
虽然最近的结果强调了染色质可及性和基因表达等细胞状态特征在预测 TF 结合方面的作用，但我们的结果强调，对于某些 TFs，结合位点的 DNA 序列包含大量细胞类型特异性的基序。
