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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Main Authors: 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
Format: Article
Language:English
Published: BMC 2025-03-01
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.
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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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