IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/3001491.html
   My bibliography  Save this article

Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network

Author

Listed:
  • Arantxa Urchueguía
  • Luca Galbusera
  • Dany Chauvin
  • Gwendoline Bellement
  • Thomas Julou
  • Erik van Nimwegen

Abstract

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter’s sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network.Genome-wide flow cytometry measurements reveal that gene expression noise in bacteria is highly condition-dependent; while absolute noise levels of all genes decrease with growth-rate, theoretical modeling shows that the relative noise levels of different genes are determined by the propagation of noise through the gene regulatory network (GRN). Thus GRN structure controls not only mean expression but also noise levels.

Suggested Citation

  • Arantxa Urchueguía & Luca Galbusera & Dany Chauvin & Gwendoline Bellement & Thomas Julou & Erik van Nimwegen, 2021. "Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network," PLOS Biology, Public Library of Science, vol. 19(12), pages 1-22, December.
  • Handle: RePEc:plo:pbio00:3001491
    DOI: 10.1371/journal.pbio.3001491
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001491
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.3001491&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.3001491?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. William J. Blake & Mads KÆrn & Charles R. Cantor & J. J. Collins, 2003. "Noise in eukaryotic gene expression," Nature, Nature, vol. 422(6932), pages 633-637, April.
    2. Christopher V. Rao & Denise M. Wolf & Adam P. Arkin, 2002. "Control, exploitation and tolerance of intracellular noise," Nature, Nature, vol. 420(6912), pages 231-237, November.
    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. Burton W Andrews & Pablo A Iglesias, 2007. "An Information-Theoretic Characterization of the Optimal Gradient Sensing Response of Cells," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-9, August.
    2. Thounaojam, Umeshkanta Singh, 2022. "Stochastic chaos in chemical Lorenz system: Interplay of intrinsic noise and nonlinearity," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    3. Benjamin B Kaufmann & Qiong Yang & Jerome T Mettetal & Alexander van Oudenaarden, 2007. "Heritable Stochastic Switching Revealed by Single-Cell Genealogy," PLOS Biology, Public Library of Science, vol. 5(9), pages 1-8, September.
    4. Li, Hongying & Yao, Chengli, 2017. "The influence of internal noise on the detection of hormonal signal with the existence of external noise in a cell system," Applied Mathematics and Computation, Elsevier, vol. 314(C), pages 1-6.
    5. Mohammad Soltani & Cesar A Vargas-Garcia & Duarte Antunes & Abhyudai Singh, 2016. "Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-23, August.
    6. Junjie Luo & Jun Wang & Ting Martin Ma & Zhirong Sun, 2010. "Reverse Engineering of Bacterial Chemotaxis Pathway via Frequency Domain Analysis," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-8, March.
    7. Ankit Gupta & Mustafa Khammash, 2022. "Frequency spectra and the color of cellular noise," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    8. Chih-Yuan Hsu & Bor-Sen Chen, 2016. "Systematic Design of a Metal Ion Biosensor: A Multi-Objective Optimization Approach," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-16, November.
    9. Lucy Ham & Megan A. Coomer & Kaan Öcal & Ramon Grima & Michael P. H. Stumpf, 2024. "A stochastic vs deterministic perspective on the timing of cellular events," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    10. Blasi, Monica Francesca & Casorelli, Ida & Colosimo, Alfredo & Blasi, Francesco Simone & Bignami, Margherita & Giuliani, Alessandro, 2005. "A recursive network approach can identify constitutive regulatory circuits in gene expression data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 349-370.
    11. Chunjuan Zhu & Zibo Chen & Qiwen Sun, 2022. "Stochastic Transcription with Alterable Synthesis Rates," Mathematics, MDPI, vol. 10(13), pages 1-20, June.
    12. Valenti, D. & Tranchina, L. & Brai, M. & Caruso, A. & Cosentino, C. & Spagnolo, B., 2008. "Environmental metal pollution considered as noise: Effects on the spatial distribution of benthic foraminifera in two coastal marine areas of Sicily (Southern Italy)," Ecological Modelling, Elsevier, vol. 213(3), pages 449-462.
    13. Tobias May & Lee Eccleston & Sabrina Herrmann & Hansjörg Hauser & Jorge Goncalves & Dagmar Wirth, 2008. "Bimodal and Hysteretic Expression in Mammalian Cells from a Synthetic Gene Circuit," PLOS ONE, Public Library of Science, vol. 3(6), pages 1-7, June.
    14. Seyed Yahya Anvar & Allan Tucker & Veronica Vinciotti & Andrea Venema & Gert-Jan B van Ommen & Silvere M van der Maarel & Vered Raz & Peter A C ‘t Hoen, 2011. "Interspecies Translation of Disease Networks Increases Robustness and Predictive Accuracy," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-14, November.
    15. Michael W Klymkowsky & Kathy Garvin-Doxas, 2008. "Recognizing Student Misconceptions through Ed's Tools and the Biology Concept Inventory," PLOS Biology, Public Library of Science, vol. 6(1), pages 1-4, January.
    16. Diana Monteoliva & Christina B McCarthy & Luis Diambra, 2013. "Noise Minimisation in Gene Expression Switches," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    17. Elijah Roberts & Andrew Magis & Julio O Ortiz & Wolfgang Baumeister & Zaida Luthey-Schulten, 2011. "Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-21, March.
    18. G.-W. Weber & P. Taylan & S. Alparslan-Gök & S. Özöğür-Akyüz & B. Akteke-Öztürk, 2008. "Optimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximation," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 284-318, December.
    19. Marc S Sherman & Barak A Cohen, 2014. "A Computational Framework for Analyzing Stochasticity in Gene Expression," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-13, May.
    20. Chen, Aimin & Tian, Tianhai & Chen, Yiren & Zhou, Tianshou, 2022. "Stochastic analysis of a complex gene-expression model," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

    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:plo:pbio00:3001491. 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: plosbiology (email available below). General contact details of provider: https://journals.plos.org/plosbiology/ .

    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.