IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-49231-y.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-49231-y
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-49231-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Donghui Choe & Connor A. Olson & Richard Szubin & Hannah Yang & Jaemin Sung & Adam M. Feist & Bernhard O. Palsson, 2024. "Advancing the scale of synthetic biology via cross-species transfer of cellular functions enabled by iModulon engraftment," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Xinrui Zhou & Wan Yi Seow & Norbert Ha & Teh How Cheng & Lingfan Jiang & Jeeranan Boonruangkan & Jolene Jie Lin Goh & Shyam Prabhakar & Nigel Chou & Kok Hao Chen, 2024. "Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Matthew A. Lawlor & Weihuan Cao & Christopher E. Ellison, 2021. "A transposon expression burst accompanies the activation of Y-chromosome fertility genes during Drosophila spermatogenesis," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    5. Mario A Marchisio & Jörg Stelling, 2011. "Automatic Design of Digital Synthetic Gene Circuits," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-13, February.
    6. Allen W. Lynch & Myles Brown & Clifford A. Meyer, 2023. "Multi-batch single-cell comparative atlas construction by deep learning disentanglement," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    7. Singh, Vijai & Chaudhary, Dharmendra Kumar & Mani, Indra & Dhar, Pawan Kumar, 2016. "Recent advances and challenges of the use of cyanobacteria towards the production of biofuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1-10.
    8. Vincent Charron-Lamoureux & Lounès Haroune & Maude Pomerleau & Léo Hall & Frédéric Orban & Julie Leroux & Adrien Rizzi & Jean-Sébastien Bourassa & Nicolas Fontaine & Élodie V. d’Astous & Philippe Daup, 2023. "Pulcherriminic acid modulates iron availability and protects against oxidative stress during microbial interactions," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    9. Alexander Kroll & Yvan Rousset & Xiao-Pan Hu & Nina A. Liebrand & Martin J. Lercher, 2023. "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    10. Yichao Han & Wanji Li & Alden Filko & Jingyao Li & Fuzhong Zhang, 2023. "Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    11. Guido Zampieri & Supreeta Vijayakumar & Elisabeth Yaneske & Claudio Angione, 2019. "Machine and deep learning meet genome-scale metabolic modeling," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-24, July.
    12. Leonardo Sportelli & Daniel P. Eisenberg & Roberta Passiatore & Enrico D’Ambrosio & Linda A. Antonucci & Jasmine S. Bettina & Qiang Chen & Aaron L. Goldman & Michael D. Gregory & Kira Griffiths & Thom, 2024. "Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    13. Brandon Monier & Adam McDermaid & Cankun Wang & Jing Zhao & Allison Miller & Anne Fennell & Qin Ma, 2019. "IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-15, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49231-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.