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Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria

Author

Listed:
  • Travis L. LaFleur

    (Pennsylvania State University)

  • Ayaan Hossain

    (Pennsylvania State University)

  • Howard M. Salis

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

Abstract

Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combine massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ70 promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32829-5
    DOI: 10.1038/s41467-022-32829-5
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    References listed on IDEAS

    as
    1. Maarten Van Brempt & Jim Clauwaert & Friederike Mey & Michiel Stock & Jo Maertens & Willem Waegeman & Marjan De Mey, 2020. "Predictive design of sigma factor-specific promoters," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    2. Amin Espah Borujeni & Jing Zhang & Hamid Doosthosseini & Alec A. K. Nielsen & Christopher A. Voigt, 2020. "Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage," Nature Communications, Nature, vol. 11(1), pages 1-18, December.
    3. Avihu H. Yona & Eric J. Alm & Jeff Gore, 2018. "Random sequences rapidly evolve into de novo promoters," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    4. Timothy C. Yu & Winnie L. Liu & Marcia S. Brinck & Jessica E. Davis & Jeremy Shek & Grace Bower & Tal Einav & Kimberly D. Insigne & Rob Phillips & Sriram Kosuri & Guillaume Urtecho, 2021. "Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
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    Cited by:

    1. Bin Shao & Jiawei Yan, 2024. "A long-context language model for deciphering and generating bacteriophage genomes," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    2. Charlotte Cautereels & Jolien Smets & Peter Bircham & Dries De Ruysscher & Anna Zimmermann & Peter De Rijk & Jan Steensels & Anton Gorkovskiy & Joleen Masschelein & Kevin J. Verstrepen, 2024. "Combinatorial optimization of gene expression through recombinase-mediated promoter and terminator shuffling in yeast," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Peter J. Diebold & Matthew W. Rhee & Qiaojuan Shi & Nguyen Vinh Trung & Fayaz Umrani & Sheraz Ahmed & Vandana Kulkarni & Prasad Deshpande & Mallika Alexander & Ngo Hoa & Nicholas A. Christakis & Najee, 2023. "Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. Daniel P. Cetnar & Ayaan Hossain & Grace E. Vezeau & Howard M. Salis, 2024. "Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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