Precise engineering of gene expression by editing plasticity
Abstract Background Identifying transcriptional cis-regulatory elements (CREs) and understanding their role in gene expression are essential for the precise manipulation of gene expression and associated phenotypes. This knowledge is fundamental for advancing genetic engineering and improving crop t...
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| Language: | English |
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BMC
2025-03-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03516-7 |
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| author | Yang Qiu Lifen Liu Jiali Yan Xianglei Xiang Shouzhe Wang Yun Luo Kaixuan Deng Jieting Xu Minliang Jin Xiaoyu Wu Liwei Cheng Ying Zhou Weibo Xie Hai-Jun Liu Alisdair R. Fernie Xuehai Hu Jianbing Yan |
| author_facet | Yang Qiu Lifen Liu Jiali Yan Xianglei Xiang Shouzhe Wang Yun Luo Kaixuan Deng Jieting Xu Minliang Jin Xiaoyu Wu Liwei Cheng Ying Zhou Weibo Xie Hai-Jun Liu Alisdair R. Fernie Xuehai Hu Jianbing Yan |
| author_sort | Yang Qiu |
| collection | DOAJ |
| description | Abstract Background Identifying transcriptional cis-regulatory elements (CREs) and understanding their role in gene expression are essential for the precise manipulation of gene expression and associated phenotypes. This knowledge is fundamental for advancing genetic engineering and improving crop traits. Results We here demonstrate that CREs can be accurately predicted and utilized to precisely regulate gene expression beyond the range of natural variation. We firstly build two sequence-to-expression deep learning models to respectively identify distal and proximal CREs by combining them with interpretability methods in multiple crops. A large number of distal CREs are verified for enhancer activity in vitro using UMI-STARR-seq on 12,000 synthesized sequences. These comprehensively characterized CREs and their precisely predicted effects further contribute to the design of in silico editing schemes for precise engineering of gene expression. We introduce a novel concept of “editingplasticity” to evaluate the potential of promoter editing to alter expression of each gene. As a proof of concept, both exhaustive prediction and random knockout mutants are analyzed within the promoter region of ZmVTE4, a key gene affecting α-tocopherol content in maize. A high degree of agreement between predicted and observed expression is observed, extending the range of natural variation and thereby allowing the creation of an optimal phenotype. Conclusions Our study provides a robust computational framework that advances knowledge-guided gene editing for precise regulation of gene expression and crop improvement. By reliably predicting and validating CREs, we offer a tool for targeted genetic modifications, enhancing desirable traits in crops. |
| format | Article |
| id | doaj-art-477fb0694f4d4f9d8af3beaef5595bc2 |
| institution | DOAJ |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-477fb0694f4d4f9d8af3beaef5595bc22025-08-20T02:56:20ZengBMCGenome Biology1474-760X2025-03-0126112810.1186/s13059-025-03516-7Precise engineering of gene expression by editing plasticityYang Qiu0Lifen Liu1Jiali Yan2Xianglei Xiang3Shouzhe Wang4Yun Luo5Kaixuan Deng6Jieting Xu7Minliang Jin8Xiaoyu Wu9Liwei Cheng10Ying Zhou11Weibo Xie12Hai-Jun Liu13Alisdair R. Fernie14Xuehai Hu15Jianbing Yan16National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityHubei Hongshan LaboratoryNational Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityHubei Hongshan LaboratoryNational Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityNational Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityHubei Hongshan LaboratoryWIMI Biotechnology Co., Ltd.WIMI Biotechnology Co., Ltd.WIMI Biotechnology Co., Ltd.WIMI Biotechnology Co., Ltd.Institute of Agricultural Sciences of Xishuangbanna Prefecture of Yunnan ProvinceNational Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityYazhouwan National LaboratoryDepartment of Molecular Physiology, Max Planck Institute of Molecular Plant PhysiologyHubei Hongshan LaboratoryNational Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityAbstract Background Identifying transcriptional cis-regulatory elements (CREs) and understanding their role in gene expression are essential for the precise manipulation of gene expression and associated phenotypes. This knowledge is fundamental for advancing genetic engineering and improving crop traits. Results We here demonstrate that CREs can be accurately predicted and utilized to precisely regulate gene expression beyond the range of natural variation. We firstly build two sequence-to-expression deep learning models to respectively identify distal and proximal CREs by combining them with interpretability methods in multiple crops. A large number of distal CREs are verified for enhancer activity in vitro using UMI-STARR-seq on 12,000 synthesized sequences. These comprehensively characterized CREs and their precisely predicted effects further contribute to the design of in silico editing schemes for precise engineering of gene expression. We introduce a novel concept of “editingplasticity” to evaluate the potential of promoter editing to alter expression of each gene. As a proof of concept, both exhaustive prediction and random knockout mutants are analyzed within the promoter region of ZmVTE4, a key gene affecting α-tocopherol content in maize. A high degree of agreement between predicted and observed expression is observed, extending the range of natural variation and thereby allowing the creation of an optimal phenotype. Conclusions Our study provides a robust computational framework that advances knowledge-guided gene editing for precise regulation of gene expression and crop improvement. By reliably predicting and validating CREs, we offer a tool for targeted genetic modifications, enhancing desirable traits in crops.https://doi.org/10.1186/s13059-025-03516-7CREDeep learningUMI-STARR-seqPrecise regulationInsilico editing schemeEditing plasticity |
| spellingShingle | Yang Qiu Lifen Liu Jiali Yan Xianglei Xiang Shouzhe Wang Yun Luo Kaixuan Deng Jieting Xu Minliang Jin Xiaoyu Wu Liwei Cheng Ying Zhou Weibo Xie Hai-Jun Liu Alisdair R. Fernie Xuehai Hu Jianbing Yan Precise engineering of gene expression by editing plasticity Genome Biology CRE Deep learning UMI-STARR-seq Precise regulation Insilico editing scheme Editing plasticity |
| title | Precise engineering of gene expression by editing plasticity |
| title_full | Precise engineering of gene expression by editing plasticity |
| title_fullStr | Precise engineering of gene expression by editing plasticity |
| title_full_unstemmed | Precise engineering of gene expression by editing plasticity |
| title_short | Precise engineering of gene expression by editing plasticity |
| title_sort | precise engineering of gene expression by editing plasticity |
| topic | CRE Deep learning UMI-STARR-seq Precise regulation Insilico editing scheme Editing plasticity |
| url | https://doi.org/10.1186/s13059-025-03516-7 |
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