Scholarlink

<p>Systematically designing regulatory elements for precise gene expression control remains a central challenge in genomics and synthetic biology. Here we introduce DNA-Diffusion, a generative artificial intelligence framework that uses machine learning trained on DNA accessibility data from diverse cell lines to design compact regulatory elements with cell-type-specific activity. We show that DNA-Diffusion generates 200-base-pair synthetic elements that recapitulate endogenous transcription factor binding grammar while exhibiting enhanced cell-type specificity. We validated these elements using a 5,850-element STARR-seq library across three cell lines. Moreover, we demonstrated successful endogenous gene modulation using EXTRA-seq, reactivating <i>AXIN2</i>, a leukemia-protective gene, in its native genomic context. Our approach outperforms existing computational methods in balancing functional activity with cell-type specificity while maintaining sequence diversity. This work establishes DNA-Diffusion as a powerful tool for engineering compact, highly specific regulatory elements crucial for advancing gene therapies and understanding gene regulation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Designing synthetic regulatory elements using the generative AI framework DNA-Diffusion

  • 期刊论文

  • 发表日期：

    2025年12月23日

查看全文
  • Lucas Ferreira DaSilva,
  • Simon Senan,
  • Judith F. Kribelbauer-Swietek,
  • Zain Munir Patel,
  • Lithin Karmel Louis,
  • Aniketh Janardhan Reddy,
  • Sameer Gabbita,
  • Jonathan D. Rosen,
  • Zach Nussbaum,
  • César Miguel Valdez Córdova,
  • Aaron Wenteler,
  • Noah Weber,
  • Tin M. Tunjic,
  • Martino Mansoldo,
  • Talha Ahmad Khan,
  • Gue-Ho Hwang,
  • Vincent Gardeux,
  • David T. Humphreys,
  • Cameron Smith,
  • Matei Bejan,
  • Peter Bromley,
  • Will Connell,
  • Bart Deplancke,
  • Michael I. Love,
  • Emily S. Wong,
  • Wouter Meuleman,
  • Luca Pinello

摘要

Systematically designing regulatory elements for precise gene expression control remains a central challenge in genomics and synthetic biology. Here we introduce DNA-Diffusion, a generative artificial intelligence framework that uses machine learning trained on DNA accessibility data from diverse cell lines to design compact regulatory elements with cell-type-specific activity. We show that DNA-Diffusion generates 200-base-pair synthetic elements that recapitulate endogenous transcription factor binding grammar while exhibiting enhanced cell-type specificity. We validated these elements using a 5,850-element STARR-seq library across three cell lines. Moreover, we demonstrated successful endogenous gene modulation using EXTRA-seq, reactivating AXIN2, a leukemia-protective gene, in its native genomic context. Our approach outperforms existing computational methods in balancing functional activity with cell-type specificity while maintaining sequence diversity. This work establishes DNA-Diffusion as a powerful tool for engineering compact, highly specific regulatory elements crucial for advancing gene therapies and understanding gene regulation.