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An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells

Author

Listed:
  • Ross D. Jones

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Yili Qian

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Velia Siciliano

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Instituto Italiano di Tecnologia)

  • Breanna DiAndreth

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Jin Huh

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Ron Weiss

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Domitilla Del Vecchio

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

Abstract

Synthetic biology has the potential to bring forth advanced genetic devices for applications in healthcare and biotechnology. However, accurately predicting the behavior of engineered genetic devices remains difficult due to lack of modularity, wherein a device’s output does not depend only on its intended inputs but also on its context. One contributor to lack of modularity is loading of transcriptional and translational resources, which can induce coupling among otherwise independently-regulated genes. Here, we quantify the effects of resource loading in engineered mammalian genetic systems and develop an endoribonuclease-based feedforward controller that can adapt the expression level of a gene of interest to significant resource loading in mammalian cells. Near-perfect adaptation to resource loads is facilitated by high production and catalytic rates of the endoribonuclease. Our design is portable across cell lines and enables predictable tuning of controller function. Ultimately, our controller is a general-purpose device for predictable, robust, and context-independent control of gene expression.

Suggested Citation

  • Ross D. Jones & Yili Qian & Velia Siciliano & Breanna DiAndreth & Jin Huh & Ron Weiss & Domitilla Del Vecchio, 2020. "An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells," Nature Communications, Nature, vol. 11(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19126-9
    DOI: 10.1038/s41467-020-19126-9
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    Cited by:

    1. Anna-Maria Makri Pistikou & Glenn A. O. Cremers & Bryan L. Nathalia & Theodorus J. Meuleman & Bas W. A. Bögels & Bruno V. Eijkens & Anne Dreu & Maarten T. H. Bezembinder & Oscar M. J. A. Stassen & Car, 2023. "Engineering a scalable and orthogonal platform for synthetic communication in mammalian cells," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Ross D. Jones & Yili Qian & Katherine Ilia & Benjamin Wang & Michael T. Laub & Domitilla Del Vecchio & Ron Weiss, 2022. "Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Chenrui Qin & Yanhui Xiang & Jie Liu & Ruilin Zhang & Ziming Liu & Tingting Li & Zhi Sun & Xiaoyi Ouyang & Yeqing Zong & Haoqian M. Zhang & Qi Ouyang & Long Qian & Chunbo Lou, 2023. "Precise programming of multigene expression stoichiometry in mammalian cells by a modular and programmable transcriptional system," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Simeon D. Castle & Michiel Stock & Thomas E. Gorochowski, 2024. "Engineering is evolution: a perspective on design processes to engineer biology," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    5. Roberto Di Blasi & Mara Pisani & Fabiana Tedeschi & Masue M. Marbiah & Karen Polizzi & Simone Furini & Velia Siciliano & Francesca Ceroni, 2023. "Resource-aware construct design in mammalian cells," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    6. Breanna DiAndreth & Noreen Wauford & Eileen Hu & Sebastian Palacios & Ron Weiss, 2022. "PERSIST platform provides programmable RNA regulation using CRISPR endoRNases," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Carlos Barajas & Hsin-Ho Huang & Jesse Gibson & Luis Sandoval & Domitilla Vecchio, 2022. "Feedforward growth rate control mitigates gene activation burden," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. Yuanli Gao & Lei Wang & Baojun Wang, 2023. "Customizing cellular signal processing by synthetic multi-level regulatory circuits," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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