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High-resolution temporal profiling of E. coli transcriptional response

Author

Listed:
  • Arianna Miano

    (University of California San Diego)

  • Kevin Rychel

    (University of California San Diego)

  • Andrew Lezia

    (University of California San Diego)

  • Anand Sastry

    (University of California San Diego)

  • Bernhard Palsson

    (University of California San Diego
    Technical University of Denmark)

  • Jeff Hasty

    (University of California San Diego
    University of California San Diego
    University of California San Diego)

Abstract

Understanding how cells dynamically adapt to their environment is a primary focus of biology research. Temporal information about cellular behavior is often limited by both small numbers of data time-points and the methods used to analyze this data. Here, we apply unsupervised machine learning to a data set containing the activity of 1805 native promoters in E. coli measured every 10 minutes in a high-throughput microfluidic device via fluorescence time-lapse microscopy. Specifically, this data set reveals E. coli transcriptome dynamics when exposed to different heavy metal ions. We use a bioinformatics pipeline based on Independent Component Analysis (ICA) to generate insights and hypotheses from this data. We discovered three primary, time-dependent stages of promoter activation to heavy metal stress (fast, intermediate, and steady). Furthermore, we uncovered a global strategy E. coli uses to reallocate resources from stress-related promoters to growth-related promoters following exposure to heavy metal stress.

Suggested Citation

  • Arianna Miano & Kevin Rychel & Andrew Lezia & Anand Sastry & Bernhard Palsson & Jeff Hasty, 2023. "High-resolution temporal profiling of E. coli transcriptional response," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43173-7
    DOI: 10.1038/s41467-023-43173-7
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    References listed on IDEAS

    as
    1. Kevin Rychel & Anand V. Sastry & Bernhard O. Palsson, 2020. "Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Alexandra Gennaris & Benjamin Ezraty & Camille Henry & Rym Agrebi & Alexandra Vergnes & Emmanuel Oheix & Julia Bos & Pauline Leverrier & Leon Espinosa & Joanna Szewczyk & Didier Vertommen & Olga Iranz, 2015. "Repairing oxidized proteins in the bacterial envelope using respiratory chain electrons," Nature, Nature, vol. 528(7582), pages 409-412, December.
    3. Anand V. Sastry & Ye Gao & Richard Szubin & Ying Hefner & Sibei Xu & Donghyuk Kim & Kumari Sonal Choudhary & Laurence Yang & Zachary A. King & Bernhard O. Palsson, 2019. "The Escherichia coli transcriptome mostly consists of independently regulated modules," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
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