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

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.

Authors

Institutions

Publication Details

Journal
Nucleic Acids Research
Published
2024-10-18
DOI
https://doi.org/10.1093/nar/gkae912
Citations
13
Primary Topic
Chemical Synthesis and Analysis
Type
article
Field-Weighted Citation Impact
3.04

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Chugang Mei, Junwei Cao, Choulin Chen, Lingna Xu et al.
13 citations
Nucleic Acids Research
Chemical Synthesis and Analysis
3.04
article

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

Chugang Mei, Junwei Cao, Choulin Chen, Lingna Xu, Yuqi Zhu, Fangjie Zhu, Jian‐Quan Ni, Zhaohong Li, Hongyan Wang, Changwei Shao, Bo Peng, Xinhao Ma, Hong Yi, Erwei Zuo, Yun Chen, Yilong Yao, Bratati De, Jing Wang, Binghua Liu, Zhonglin Tang, Biao Deng, Tongjun Sun, Yuanyuan Zhang, Xi Chen, Ningli Wang, Long Xie, Jiaying Li, Gang Liu, Yuting Chen, Ranran Liu, Guirong Wang, Yuwen Liu, Shenghua Qin, Tian Li, Qian Zhang, Yongzhou Bao, Qian Liu
article en
13 citations

Abstract

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.

Nucleic Acids ResearchVol. 52(21)
Inner Mongolia Agricultural University (CN), Beijing Tongren Hospital (CN), Shanxi Medical University (CN), Capital Medical University (CN), Chinese Academy of Sciences (CN), Agricultural Genomics Institute at Shenzhen (CN), Czech Academy of Sciences, Institute of Microbiology (CZ), Chinese Academy of Agricultural Sciences (CN), Molecular Oncology (United States) (US), Ministry of Agriculture and Rural Affairs (CN), National Beef Cattle Industrial Technology System (CN), Chinese Academy of Fishery Sciences (CN), Fujian Agriculture and Forestry University (CN), Northwest A&F University (CN), Tsinghua University (CN)
Ministry of Science and Technology, National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, National Key Research and Development Program of China
Openalex Percentile: Top 8%
Chemical Synthesis and Analysis
3.04
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.