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<p>Predictable control of gene expression is essential for building genetic circuits and improving metabolic pathways, but conventional promoter libraries often behave unpredictably when genes are combined. Here we develop CRISPR-Activated Promoter-based Orthogonal expression (CAPO), a quantitative platform for controlling multiple genes in yeast. CAPO uses synthetic CRISPR-activated promoters that remain silent until matching guide RNAs recruit dCas9-VPR. We tune each gene by varying guide RNA abundance with defined T7 promoters, while keeping regulatory channels orthogonal. CAPO reaches expression levels comparable to strong native yeast promoters, maintains low background activity, and preserves promoter-strength order across different genes. We apply CAPO to program broad fluorescence color outputs and to rapidly optimize lycopene and 3-hydroxypropionic acid biosynthesis. These results establish CAPO as a scalable platform for predictable engineering of eukaryotic gene networks.</p>

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A decoupled transcription platform enables tunable and predictable gene expression in yeast

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Yujie Chen,
  • Hui Li,
  • Lin Duan,
  • Yang Liu,
  • Jingrui Yan,
  • Haoyang Chen,
  • Jianguo Yang

摘要

Predictable control of gene expression is essential for building genetic circuits and improving metabolic pathways, but conventional promoter libraries often behave unpredictably when genes are combined. Here we develop CRISPR-Activated Promoter-based Orthogonal expression (CAPO), a quantitative platform for controlling multiple genes in yeast. CAPO uses synthetic CRISPR-activated promoters that remain silent until matching guide RNAs recruit dCas9-VPR. We tune each gene by varying guide RNA abundance with defined T7 promoters, while keeping regulatory channels orthogonal. CAPO reaches expression levels comparable to strong native yeast promoters, maintains low background activity, and preserves promoter-strength order across different genes. We apply CAPO to program broad fluorescence color outputs and to rapidly optimize lycopene and 3-hydroxypropionic acid biosynthesis. These results establish CAPO as a scalable platform for predictable engineering of eukaryotic gene networks.