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Escherichia coli can survive stress by noisy growth modulation

Author

Listed:
  • Om Patange

    (University of Cambridge
    University of Cambridge)

  • Christian Schwall

    (University of Cambridge
    University of Cambridge)

  • Matt Jones

    (University of Cambridge)

  • Casandra Villava

    (University of Cambridge)

  • Douglas A. Griffith

    (University of Cambridge)

  • Andrew Phillips

    (Microsoft Research)

  • James C. W. Locke

    (University of Cambridge
    University of Cambridge
    Microsoft Research)

Abstract

Gene expression can be noisy, as can the growth of single cells. Such cell-to-cell variation has been implicated in survival strategies for bacterial populations. However, it remains unclear how single cells couple gene expression with growth to implement these strategies. Here, we show how noisy expression of a key stress-response regulator, RpoS, allows E. coli to modulate its growth dynamics to survive future adverse environments. We reveal a dynamic positive feedback loop between RpoS and growth rate that produces multi-generation RpoS pulses. We do so experimentally using single-cell, time-lapse microscopy and microfluidics and theoretically with a stochastic model. Next, we demonstrate that E. coli prepares for sudden stress by entering prolonged periods of slow growth mediated by RpoS. This dynamic phenotype is captured by the RpoS-growth feedback model. Our synthesis of noisy gene expression, growth, and survival paves the way for further exploration of functional phenotypic variability.

Suggested Citation

  • Om Patange & Christian Schwall & Matt Jones & Casandra Villava & Douglas A. Griffith & Andrew Phillips & James C. W. Locke, 2018. "Escherichia coli can survive stress by noisy growth modulation," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07702-z
    DOI: 10.1038/s41467-018-07702-z
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    Cited by:

    1. Jean-Baptiste Lugagne & Caroline M. Blassick & Mary J. Dunlop, 2024. "Deep model predictive control of gene expression in thousands of single cells," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Lucas Henrion & Juan Andres Martinez & Vincent Vandenbroucke & Mathéo Delvenne & Samuel Telek & Andrew Zicler & Alexander Grünberger & Frank Delvigne, 2023. "Fitness cost associated with cell phenotypic switching drives population diversification dynamics and controllability," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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