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Engineered systems of inducible anti-repressors for the next generation of biological programming

Author

Listed:
  • Thomas M. Groseclose

    (School of Chemical & Biomolecular Engineering)

  • Ronald E. Rondon

    (School of Chemical & Biomolecular Engineering)

  • Zachary D. Herde

    (School of Chemical & Biomolecular Engineering)

  • Carlos A. Aldrete

    (School of Chemical & Biomolecular Engineering)

  • Corey J. Wilson

    (School of Chemical & Biomolecular Engineering)

Abstract

Traditionally engineered genetic circuits have almost exclusively used naturally occurring transcriptional repressors. Recently, non-natural transcription factors (repressors) have been engineered and employed in synthetic biology with great success. However, transcriptional anti-repressors have largely been absent with regard to the regulation of genes in engineered genetic circuits. Here, we present a workflow for engineering systems of non-natural anti-repressors. In this study, we create 41 inducible anti-repressors. This collection of transcription factors respond to two distinct ligands, fructose (anti-FruR) or D-ribose (anti-RbsR); and were complemented by 14 additional engineered anti-repressors that respond to the ligand isopropyl β-d-1-thiogalactopyranoside (anti-LacI). In turn, we use this collection of anti-repressors and complementary genetic architectures to confer logical control over gene expression. Here, we achieved all NOT oriented logical controls (i.e., NOT, NOR, NAND, and XNOR). The engineered transcription factors and corresponding series, parallel, and series-parallel genetic architectures represent a nascent anti-repressor based transcriptional programming structure.

Suggested Citation

  • Thomas M. Groseclose & Ronald E. Rondon & Zachary D. Herde & Carlos A. Aldrete & Corey J. Wilson, 2020. "Engineered systems of inducible anti-repressors for the next generation of biological programming," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18302-1
    DOI: 10.1038/s41467-020-18302-1
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    Cited by:

    1. Brian D. Huang & Thomas M. Groseclose & Corey J. Wilson, 2022. "Transcriptional programming in a Bacteroides consortium," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Yang Gao & Yuchen Zhou & Xudong Ji & Austin J. Graham & Christopher M. Dundas & Ismar E. Miniel Mahfoud & Bailey M. Tibbett & Benjamin Tan & Gina Partipilo & Ananth Dodabalapur & Jonathan Rivnay & Ben, 2024. "A hybrid transistor with transcriptionally controlled computation and plasticity," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Trevor R. Simmons & Gina Partipilo & Ryan Buchser & Anna C. Stankes & Rashmi Srivastava & Darian Chiu & Benjamin K. Keitz & Lydia M. Contreras, 2024. "Rewiring native post-transcriptional global regulators to achieve designer, multi-layered genetic circuits," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Brian D. Huang & Dowan Kim & Yongjoon Yu & Corey J. Wilson, 2024. "Engineering intelligent chassis cells via recombinase-based MEMORY circuits," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Andrew E. Short & Dowan Kim & Prasaad T. Milner & Corey J. Wilson, 2023. "Next generation synthetic memory via intercepting recombinase function," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

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