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Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems

Author

Listed:
  • Timothy C. Yu

    (University of California)

  • Winnie L. Liu

    (University of California)

  • Marcia S. Brinck

    (University of California)

  • Jessica E. Davis

    (University of California)

  • Jeremy Shek

    (University of California)

  • Grace Bower

    (University of California)

  • Tal Einav

    (California Institute of Technology)

  • Kimberly D. Insigne

    (University of California)

  • Rob Phillips

    (California Institute of Technology
    California Institute of Technology
    California Institute of Technology)

  • Sriram Kosuri

    (University of California
    UCLA-DOE Institute for Genomics and Proteomics
    University of California, Los Angeles
    University of California, Los Angeles)

  • Guillaume Urtecho

    (University of California)

Abstract

A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8269 rationally designed, IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. We then fit a statistical mechanics model to measured expression that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three alternative promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.

Suggested Citation

  • Timothy C. Yu & Winnie L. Liu & Marcia S. Brinck & Jessica E. Davis & Jeremy Shek & Grace Bower & Tal Einav & Kimberly D. Insigne & Rob Phillips & Sriram Kosuri & Guillaume Urtecho, 2021. "Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20094-3
    DOI: 10.1038/s41467-020-20094-3
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    Cited by:

    1. Pengcheng Zhang & Haochen Wang & Hanwen Xu & Lei Wei & Liyang Liu & Zhirui Hu & Xiaowo Wang, 2023. "Deep flanking sequence engineering for efficient promoter design using DeepSEED," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Travis L. LaFleur & Ayaan Hossain & Howard M. Salis, 2022. "Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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