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Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics

Author

Listed:
  • Aqib Hasnain

    (University of California Santa Barbara)

  • Shara Balakrishnan

    (University of California Santa Barbara)

  • Dennis M. Joshy

    (University of California Santa Barbara)

  • Jen Smith

    (University of California Santa Barbara)

  • Steven B. Haase

    (Duke University)

  • Enoch Yeung

    (University of California Santa Barbara)

Abstract

A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library’s ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37897-9
    DOI: 10.1038/s41467-023-37897-9
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    References listed on IDEAS

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    1. Atte Aalto & Lauri Viitasaari & Pauliina Ilmonen & Laurent Mombaerts & Jorge Gonçalves, 2020. "Gene regulatory network inference from sparsely sampled noisy data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    2. 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.
    3. Jake P Taylor-King & Asbjørn N Riseth & Will Macnair & Manfred Claassen, 2020. "Dynamic distribution decomposition for single-cell snapshot time series identifies subpopulations and trajectories during iPSC reprogramming," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-21, January.
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