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A progesterone biosensor derived from microbial screening

Author

Listed:
  • Chloé Grazon

    (Boston University
    University Bordeaux, CNRS, Bordeaux INP, LCPO, UMR 5629)

  • R C. Baer

    (Boston University
    Boston University)

  • Uroš Kuzmanović

    (Boston University)

  • Thuy Nguyen

    (Boston University)

  • Mingfu Chen

    (Boston University)

  • Marjon Zamani

    (Boston University)

  • Margaret Chern

    (Boston University)

  • Patricia Aquino

    (Boston University)

  • Xiaoman Zhang

    (Boston University)

  • Sébastien Lecommandoux

    (University Bordeaux, CNRS, Bordeaux INP, LCPO, UMR 5629)

  • Andy Fan

    (Boston University)

  • Mario Cabodi

    (Boston University)

  • Catherine Klapperich

    (Boston University
    Boston University)

  • Mark W. Grinstaff

    (Boston University
    Boston University
    Boston University)

  • Allison M. Dennis

    (Boston University
    Boston University)

  • James E. Galagan

    (Boston University
    Boston University
    Boston University)

Abstract

Bacteria are an enormous and largely untapped reservoir of biosensing proteins. We describe an approach to identify and isolate bacterial allosteric transcription factors (aTFs) that recognize a target analyte and to develop these TFs into biosensor devices. Our approach utilizes a combination of genomic screens and functional assays to identify and isolate biosensing TFs, and a quantum-dot Förster Resonance Energy Transfer (FRET) strategy for transducing analyte recognition into real-time quantitative measurements. We use this approach to identify a progesterone-sensing bacterial aTF and to develop this TF into an optical sensor for progesterone. The sensor detects progesterone in artificial urine with sufficient sensitivity and specificity for clinical use, while being compatible with an inexpensive and portable electronic reader for point-of-care applications. Our results provide proof-of-concept for a paradigm of microbially-derived biosensors adaptable to inexpensive, real-time sensor devices.

Suggested Citation

  • Chloé Grazon & R C. Baer & Uroš Kuzmanović & Thuy Nguyen & Mingfu Chen & Marjon Zamani & Margaret Chern & Patricia Aquino & Xiaoman Zhang & Sébastien Lecommandoux & Andy Fan & Mario Cabodi & Catherine, 2020. "A progesterone biosensor derived from microbial screening," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14942-5
    DOI: 10.1038/s41467-020-14942-5
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    Cited by:

    1. Aqib Hasnain & Shara Balakrishnan & Dennis M. Joshy & Jen Smith & Steven B. Haase & Enoch Yeung, 2023. "Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    2. Dan Xiao & Wen Zhang & Xiaoting Guo & Yidong Liu & Chunxia Hu & Shiting Guo & Zhaoqi Kang & Xianzhi Xu & Cuiqing Ma & Chao Gao & Ping Xu, 2021. "A d-2-hydroxyglutarate biosensor based on specific transcriptional regulator DhdR," Nature Communications, Nature, vol. 12(1), pages 1-14, December.

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