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Proteome allocation is linked to transcriptional regulation through a modularized transcriptome

Author

Listed:
  • Arjun Patel

    (San Diego)

  • Dominic McGrosso

    (San Diego)

  • Ying Hefner

    (San Diego)

  • Anaamika Campeau

    (San Diego)

  • Anand V. Sastry

    (San Diego)

  • Svetlana Maurya

    (San Diego)

  • Kevin Rychel

    (San Diego)

  • David J. Gonzalez

    (San Diego
    San Diego)

  • Bernhard O. Palsson

    (San Diego
    2800 Kgs)

Abstract

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

Suggested Citation

  • Arjun Patel & Dominic McGrosso & Ying Hefner & Anaamika Campeau & Anand V. Sastry & Svetlana Maurya & Kevin Rychel & David J. Gonzalez & Bernhard O. Palsson, 2024. "Proteome allocation is linked to transcriptional regulation through a modularized transcriptome," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49231-y
    DOI: 10.1038/s41467-024-49231-y
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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. Wade Winkler & Ali Nahvi & Ronald R. Breaker, 2002. "Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression," Nature, Nature, vol. 419(6910), pages 952-956, October.
    3. Wouter Saelens & Robrecht Cannoodt & Yvan Saeys, 2018. "A comprehensive evaluation of module detection methods for gene expression data," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    4. 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.
    5. Ali Ebrahim & Elizabeth Brunk & Justin Tan & Edward J. O'Brien & Donghyuk Kim & Richard Szubin & Joshua A. Lerman & Anna Lechner & Anand Sastry & Aarash Bordbar & Adam M. Feist & Bernhard O. Palsson, 2016. "Multi-omic data integration enables discovery of hidden biological regularities," Nature Communications, Nature, vol. 7(1), pages 1-9, December.
    6. Amitesh Anand & Arjun Patel & Ke Chen & Connor A. Olson & Patrick V. Phaneuf & Cameron Lamoureux & Ying Hefner & Richard Szubin & Adam M. Feist & Bernhard O. Palsson, 2022. "Laboratory evolution of synthetic electron transport system variants reveals a larger metabolic respiratory system and its plasticity," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
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