Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Cell and Developmental Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2023.1034604/full |
| _version_ | 1797902938912325632 |
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| author | Rawan Alatawneh Rawan Alatawneh Yahel Salomon Reut Eshel Reut Eshel Yaron Orenstein Yaron Orenstein Yaron Orenstein Ramon Y. Birnbaum Ramon Y. Birnbaum |
| author_facet | Rawan Alatawneh Rawan Alatawneh Yahel Salomon Reut Eshel Reut Eshel Yaron Orenstein Yaron Orenstein Yaron Orenstein Ramon Y. Birnbaum Ramon Y. Birnbaum |
| author_sort | Rawan Alatawneh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T09:25:07Z |
| format | Article |
| id | doaj.art-1bc69c7123e946aa96a91a9071ca3cc7 |
| institution | Directory Open Access Journal |
| issn | 2296-634X |
| language | English |
| last_indexed | 2024-04-10T09:25:07Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj.art-1bc69c7123e946aa96a91a9071ca3cc72023-02-20T04:50:15ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2023-02-011110.3389/fcell.2023.10346041034604Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networksRawan Alatawneh0Rawan Alatawneh1Yahel Salomon2Reut Eshel3Reut Eshel4Yaron Orenstein5Yaron Orenstein6Yaron Orenstein7Ramon Y. Birnbaum8Ramon Y. Birnbaum9Department of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beer-Sheva, IsraelThe Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, IsraelSchool of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, IsraelDepartment of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beer-Sheva, IsraelThe Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, IsraelSchool of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, IsraelDepartment of Computer Science, Bar-Ilan University, Ramat Gan, IsraelThe Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, IsraelDepartment of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beer-Sheva, IsraelThe Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, IsraelDuring 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.https://www.frontiersin.org/articles/10.3389/fcell.2023.1034604/fullnon-active enhancersrepressed enhancersdeep-learningconvolution neuronal networkspredicted TF motifsinhibitory interneuron progenitors |
| spellingShingle | Rawan Alatawneh Rawan Alatawneh Yahel Salomon Reut Eshel Reut Eshel Yaron Orenstein Yaron Orenstein Yaron Orenstein Ramon Y. Birnbaum Ramon Y. Birnbaum Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks Frontiers in Cell and Developmental Biology non-active enhancers repressed enhancers deep-learning convolution neuronal networks predicted TF motifs inhibitory interneuron progenitors |
| title | Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks |
| title_full | Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks |
| title_fullStr | Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks |
| title_full_unstemmed | Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks |
| title_short | Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks |
| title_sort | deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks |
| topic | non-active enhancers repressed enhancers deep-learning convolution neuronal networks predicted TF motifs inhibitory interneuron progenitors |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2023.1034604/full |
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