增强子
计算生物学
生物
合成生物学
转录因子
类型(生物学)
基因
遗传学
细胞生物学
化学
生态学
作者
Ibrahim Ihsan Taskiran,Katina I. Spanier,Hannah Dickmänken,Niklas Kempynck,Alexandra Pančíková,Eren Can Ekşi,Gert Hulselmans,Joy N Ismail,Koen Theunis,Roel Vandepoel,Valerie Christiaens,David Mauduit,Stein Aerts
出处
期刊:Nature
[Springer Nature]
日期:2023-12-12
被引量:2
标识
DOI:10.1038/s41586-023-06936-2
摘要
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes1. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models2-6, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create 'dual-code' enhancers that target two cell types and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.
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