Controlling gene expression with deep generative design of regulatory DNA

Jan Zrimec (), Xiaozhi Fu, Azam Sheikh Muhammad, Christos Skrekas, Vykintas Jauniskis, Nora K. Speicher, Christoph S. Börlin, Vilhelm Verendel, Morteza Haghir Chehreghani, Devdatt Dubhashi, Verena Siewers, Florian David, Jens Nielsen and Aleksej Zelezniak ()
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Jan Zrimec: Chalmers University of Technology
Xiaozhi Fu: Chalmers University of Technology
Azam Sheikh Muhammad: Chalmers University of Technology
Christos Skrekas: Chalmers University of Technology
Vykintas Jauniskis: Chalmers University of Technology
Nora K. Speicher: Chalmers University of Technology
Christoph S. Börlin: Chalmers University of Technology
Vilhelm Verendel: Chalmers University of Technology
Morteza Haghir Chehreghani: Chalmers University of Technology
Devdatt Dubhashi: Chalmers University of Technology
Verena Siewers: Chalmers University of Technology
Florian David: Chalmers University of Technology
Jens Nielsen: Chalmers University of Technology
Aleksej Zelezniak: Chalmers University of Technology

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: Abstract Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32818-8

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DOI: 10.1038/s41467-022-32818-8

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