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A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity

文献类型: 外文期刊

作者: Li, Zhaohong 1 ; Zhang, Yuanyuan 1 ; Peng, Bo 3 ; Qin, Shenghua 1 ; Zhang, Qian 5 ; Chen, Yun 1 ; Chen, Choulin 1 ; Bao, Yongzhou 1 ; Zhu, Yuqi 6 ; Hong, Yi 6 ; Liu, Binghua 7 ; Liu, Qian 7 ; Xu, Lingna 1 ; Chen, Xi 8 ; Ma, Xinhao 9 ; Wang, Hongyan 7 ; Xie, Long 1 ; Yao, Yilong 10 ; Deng, Biao 1 ; Li, Jiaying 11 ; De, Baojun 12 ; Chen, Yuting 12 ; Wang, Jing 8 ; Li, Tian 13 ; Liu, Ranran 14 ; Tang, Zhonglin 10 ; Cao, Junwei 12 ; Zuo, Erwei 1 ; Mei, Chugang 9 ; Zhu, Fangjie 13 ; Shao, Changwei 7 ; Wang, Guirong 8 ; Sun, Tongjun 6 ; Wang, Ningli 11 ; Liu, Gang 5 ; Ni, Jian-Quan 3 ; Liu, Yuwen 1 ;

作者机构: 1.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Key Lab Livestock & Poultry Multi MARA, Shenzhen Branch,Guangdong Lab Lingnan Modern Agr, Buxin Rd 97, Shenzhen 518124, Peoples R China

2.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Res Ctr Anim Genome, Innovat Grp Pig Genome Design & Breeding, Buxin Rd 97, Shenzhen 518124, Peoples R China

3.Tsinghua Univ, Sch Basic Med Sci, Gene Regulatory Lab, 30 Shuangqing Rd, Beijing 100084, Peoples R China

4.Tsinghua Univ, State Key Lab Mol Oncol, 30 Shuangqing Rd, Beijing 100084, Peoples R China

5.Chinese Acad Sci, State Key Lab Mycol, Inst Microbiol, 1 Beichen West Rd, Beijing 100101, Peoples R China

6.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Genome Anal Lab, Shenzhen Branch,Guangdong Lab Lingnan Modern Agr,M, 7 Pengfei Rd, Shenzhen 518124, Peoples R China

7.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, State Key Lab Maricultural Biobreeding & Sustainab, 106 Nanjing Rd, Qingdao 266071, Shandong, Peoples R China

8.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Guangdong Lab Lingnan Modern Agr, Key Lab Synthet Biol,Agr Genom Inst Shenzhen, Buxin Rd 97, Shenzhen 518124, Peoples R China

9.Northwest A&F Univ, Coll Grassland Agr, Coll Anim Sci & Technol, Natl Beef Cattle Improvement Ctr, 3 Taicheng Rd, Yangling 712100, Shaanxi, Peoples R China

10.Chinese Acad Agr Sci, Kunpeng Inst Modern Agr Foshan, Green Hlth Aquaculture Res Ctr, Bldg 26 Lihe Technol Pk,Auxiliary Rd Xinxi Ave Sou, Foshan 528226, Peoples R China

11.Capital Med Univ, Beijing Tongren Hosp, Beijing Tongren Eye Ctr, Dept Ophthalmol,Beijing Inst Ophthalmol, Dongjiaomin lane 1, Beijing 100101, Peoples R China

12.Inner Mongolia Agr Univ, Coll Life Sci, Inner Mongolia Autonomous Reg Key Lab Biomfg, 306 Zhaowuda Rd, Hohhot 010018, Peoples R China

13.Fujian Agr & Forestry Univ FAFU, Haixia Inst Sci & Technol, Coll JUNCAO Sci & Ecol, Natl Engn Res Ctr JUNCAO, 15 Shangxiadian Rd, Fuzhou 0350002, Peoples R China

14.Chinese Acad Agr Sci, Inst Anim Sci, Beijing 100193, Peoples R China

15.Shanxi Med Univ, Tsinghua Collaborat Innovat Ctr Frontier Med, SXMU, 56 Xinjian South Rd, Taiyuan 030001, Peoples R China

期刊名称:NUCLEIC ACIDS RESEARCH ( 影响因子:13.1; 五年影响因子:16.8 )

ISSN: 0305-1048

年卷期: 2024 年 52 卷 21 期

页码:

收录情况: SCI

摘要: Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical sequence features implicating strong enhancer activity. Leveraging DREAM, we have engineered enhancers that surpass the potency of the strongest enhancer within the Drosophila genome by approximately 3.6-fold. Remarkably, these synthetic enhancers exhibited conserved functionality across species that have diverged more than billion years, indicating that DREAM was able to learn highly conserved enhancer regulatory grammar. Additionally, we designed silencers and cell line-specific enhancers using DREAM, demonstrating its versatility. Overall, our study not only introduces an interpretable approach for enhancer design but also lays out a general framework applicable to the design of other types of cis-regulatory elements. Graphical Abstract

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