Table1_Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks.xlsx

During neurogenesis, the generation and differentiation of neuronal progenitors into inhibitory gamma-aminobutyric acid-containing interneurons is dependent on the combinatorial activity of transcription factors (TFs) and their corresponding regulatory elements (REs). However, the roles of neuronal TFs and their target REs in inhibitory interneuron progenitors are not fully elucidated. Here, we developed a deep-learning-based framework to identify enriched TF motifs in gene REs (eMotif-RE), such as poised/repressed enhancers and putative silencers. Using epigenetic datasets (e.g., ATAC-seq and H3K27ac/me3 ChIP-seq) from cultured interneuron-like progenitors, we distinguished between active enhancer sequences (open chromatin with H3K27ac) and non-active enhancer sequences (open chromatin without H3K27ac). Using our eMotif-RE framework, we discovered enriched motifs of TFs such as ASCL1, SOX4, and SOX11 in the active enhancer set suggesting a cooperativity function for ASCL1 and SOX4/11 in active enhancers of neuronal progenitors. In addition, we found enriched ZEB1 and CTCF motifs in the non-active set. Using an in vivo enhancer assay, we showed that most of the tested putative REs from the non-active enhancer set have no enhancer activity. Two of the eight REs (25%) showed function as poised enhancers in the neuronal system. Moreover, mutated REs for ZEB1 and CTCF motifs increased their in vivo activity as enhancers indicating a repressive effect of ZEB1 and CTCF on these REs that likely function as repressed enhancers or silencers. Overall, our work integrates a novel framework based on deep learning together with a functional assay that elucidated novel functions of TFs and their corresponding REs. Our approach can be applied to better understand gene regulation not only in inhibitory interneuron differentiation but in other tissue and cell types.

在神经发生过程中，神经元祖细胞的生成及其向抑制性γ-氨基丁酸能中间神经元的分化，依赖于转录因子（Transcription Factors, TFs）及其对应的调控元件（Regulatory Elements, REs）的组合调控活性。然而，神经元转录因子及其靶向调控元件在抑制性中间神经元祖细胞中的具体作用尚未完全阐明。本研究开发了一种基于深度学习的框架，用于识别基因调控元件中的富集转录因子基序（eMotif-RE），这类调控元件包括预激活/阻遏型增强子以及推定的沉默子。借助培养的类中间神经元祖细胞的表观基因组数据集（例如ATAC测序（ATAC-seq）和H3K27ac/me3染色质免疫沉淀测序（ChIP-seq）），本研究区分了活性增强子序列（带有H3K27ac标记的开放染色质区域）与非活性增强子序列（无H3K27ac标记的开放染色质区域）。通过eMotif-RE框架，我们在活性增强子集合中发现了ASCL1、SOX4及SOX11等转录因子的富集基序，提示ASCL1与SOX4/11在神经元祖细胞的活性增强子中存在协同调控功能。此外，我们在非活性增强子集合中发现了ZEB1与CTCF的富集基序。通过体内增强子实验，我们发现非活性增强子集合中多数测试的推定调控元件不具备增强子活性；8个调控元件中有2个（占比25%）在神经系统中表现为预激活型增强子。进一步，对ZEB1与CTCF基序对应的调控元件进行突变后，其体内增强子活性显著提升，这表明ZEB1与CTCF对这类可能作为阻遏型增强子或沉默子的调控元件具有抑制作用。综上，本研究整合了基于深度学习的新型框架与功能实验手段，阐明了转录因子及其对应调控元件的全新功能。本研究方法不仅可用于解析抑制性中间神经元分化过程中的基因调控机制，还可推广应用于其他组织与细胞类型的基因调控研究。