Interpretable deep learning reveals the sequence rules of Hippo signaling (RNA-Seq)

How specific cells respond to signaling pathways is largely encoded in the DNA sequence. However, the sequence rules result from complex interactions between signaling and cell-type-specific transcription factors and are considered intractable by traditional methods. Here, we leverage interpretable deep learning on high-resolution data and extensive validation experiments to identify the sequence rules for the Hippo pathway in mouse trophoblast stem cells. We show that Tead4 and Yap1 engage in two types of cooperativity. First, their binding is enhanced by cell-type-specific transcription factors, including Tfap2c, in a distance-dependent manner. Second, a strictly-spaced Tead double motif is a canonical Hippo pathway element that mediates strong Tead4 cooperativity through transient protein-protein interactions on DNA. These mechanisms occur genome-wide and allow us to predict how small sequence changes alter the activity of enhancers in vivo. This illustrates the power of interpretable deep learning to decode canonical and cell type-specific sequence rules of signaling pathways. RNA-seq and Nascent RNA captured through TT-seq method (Schwalb et al. 2016) in wild-type mouse trophoblast stem cells (Singh and Gerton 2021). RNA-seq in CRISPR mutatated cell lines at genomic region of chr17:6,827,739-6,828,394 (putative enhancer for Ezrin gene consisting for Tead double motif)特定细胞对信号通路的响应模式，在很大程度上由DNA序列编码。然而，此类序列规则源于信号通路与细胞类型特异性转录因子之间的复杂相互作用，传统计算方法难以对其进行解析。本研究借助可解释深度学习（interpretable deep learning）结合高分辨率实验数据与大规模验证实验，鉴定了小鼠滋养层干细胞中Hippo信号通路的序列规则。研究发现，Tead4与Yap1存在两类协同作用模式：其一，二者的结合会受到包括Tfap2c在内的细胞类型特异性转录因子的增强，且该增强效应呈现距离依赖性；其二，严格间隔的Tead双基序是Hippo通路的经典元件，可通过DNA上的瞬时蛋白质-蛋白质相互作用介导强烈的Tead4协同效应。这些机制广泛分布于全基因组范围内，使我们能够预测微小序列改变如何在体内影响增强子的活性。这充分展示了可解释深度学习在解码信号通路经典及细胞类型特异性序列规则方面的强大能力。本研究包含两类测序数据：一是野生型小鼠滋养层干细胞的RNA测序（RNA-seq）数据，以及通过TT-seq方法捕获的新生RNA数据（Schwalb等，2016；Singh与Gerton，2021）；二是针对chr17:6,827,739-6,828,394基因组区域（包含Tead双基序的Ezrin基因推定增强子）构建的CRISPR突变细胞系的RNA测序数据。