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An autonomous microbial sensor enables long-term detection of TNT explosive in natural soil

Author

Listed:
  • Erin A. Essington

    (The Pennsylvania State University)

  • Grace E. Vezeau

    (The Pennsylvania State University)

  • Daniel P. Cetnar

    (The Pennsylvania State University)

  • Emily Grandinette

    (The Pennsylvania State University)

  • Terrence H. Bell

    (University of Toronto)

  • Howard M. Salis

    (The Pennsylvania State University
    The Pennsylvania State University
    The Pennsylvania State University)

Abstract

Microbes can be engineered to sense target chemicals for environmental and geospatial detection. However, when engineered microbes operate in real-world environments, it remains unclear how competition with natural microbes affect their performance over long time periods. Here, we engineer sensors and memory-storing genetic circuits inside the soil bacterium Bacillus subtilis to sense the TNT explosive and maintain a long-term response, using predictive models to design riboswitch sensors, tune transcription rates, and improve the genetic circuit’s dynamic range. We characterize the autonomous microbial sensor’s ability to detect TNT in a natural soil system, measuring single-cell and population-level behavior over a 28-day period. The autonomous microbial sensor activates its response by 14-fold when exposed to low TNT concentrations and maintains stable activation for over 21 days, exhibiting exponential decay dynamics at the population-level with a half-life of about 5 days. Overall, we show that autonomous microbial sensors can carry out long-term detection of an important chemical in natural soil with competitive growth dynamics serving as additional biocontainment.

Suggested Citation

  • Erin A. Essington & Grace E. Vezeau & Daniel P. Cetnar & Emily Grandinette & Terrence H. Bell & Howard M. Salis, 2024. "An autonomous microbial sensor enables long-term detection of TNT explosive in natural soil," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54866-y
    DOI: 10.1038/s41467-024-54866-y
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    References listed on IDEAS

    as
    1. 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.
    2. Grace E. Vezeau & Lipika R. Gadila & Howard M. Salis, 2023. "Automated design of protein-binding riboswitches for sensing human biomarkers in a cell-free expression system," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
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