IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1005454.html
   My bibliography  Save this article

Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling

Author

Listed:
  • Irene Otero-Muras
  • Pencho Yordanov
  • Joerg Stelling

Abstract

Bistability has important implications in signaling pathways, since it indicates a potential cell decision between alternative outcomes. We present two approaches developed in the framework of the Chemical Reaction Network Theory for easy and efficient search of multiple steady state behavior in signaling networks (both with and without mass conservation), and apply them to search for sources of bistability at different levels of the interferon signaling pathway. Different type I interferon subtypes and/or doses are known to elicit differential bioactivities (ranging from antiviral, antiproliferative to immunomodulatory activities). How different signaling outcomes can be generated through the same receptor and activating the same JAK/STAT pathway is still an open question. Here, we detect bistability at the level of early STAT signaling, showing how two different cell outcomes are achieved under or above a threshold in ligand dose or ligand-receptor affinity. This finding could contribute to explain the differential signaling (antiviral vs apoptotic) depending on interferon dose and subtype (α vs β) observed in type I interferons.Author summary: Type I interferons (IFNs) regulate a variety of cell functions, exhibiting, amongst others, antiviral, antiproliferative and immunomodulatory activities. Due to their anticancer effects, type I IFNs have a long record of applications in clinical oncology. It is still an open question how type I IFNs generate so diverse signaling outcomes by activating the same receptor at the cell membrane and triggering the same JAK/STAT pathway. It has been experimentally shown that differences in ligand affinity towards the receptor, IFN dose and receptor density are translated into different activities, but the underlying mechanisms of differential responses remain elusive. Looking for potential cell decision processes that could help answering this question, we explore the capacity for bistability at different levels of the IFN pathway. The search for bistability sources in interferon signaling is performed within the framework of Chemical Reaction Network Theory, by adapting previous results to the specific context of signaling pathways. Surprisingly, we find a source of bistability already at the early STAT signaling level. As a result, we show that the pathway has the capacity to translate a difference in affinity or IFN dose into a binary decision between High/Low or Low/High activation profiles of two IFN transcription factors (ISGF3 and STAT1-STAT1 homodimers) responsible for the upregulation of two different families of interferon stimulated genes: ISRE and GAS.

Suggested Citation

  • Irene Otero-Muras & Pencho Yordanov & Joerg Stelling, 2017. "Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-28, April.
  • Handle: RePEc:plo:pcbi00:1005454
    DOI: 10.1371/journal.pcbi.1005454
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005454
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005454&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1005454?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. Saurabh Paliwal & Pablo A. Iglesias & Kyle Campbell & Zoe Hilioti & Alex Groisman & Andre Levchenko, 2007. "MAPK-mediated bimodal gene expression and adaptive gradient sensing in yeast," Nature, Nature, vol. 446(7131), pages 46-51, March.
    2. Jörg Stelling & Steffen Klamt & Katja Bettenbrock & Stefan Schuster & Ernst Dieter Gilles, 2002. "Metabolic network structure determines key aspects of functionality and regulation," Nature, Nature, vol. 420(6912), pages 190-193, November.
    3. Björn F. C. Kafsack & Núria Rovira-Graells & Taane G. Clark & Cristina Bancells & Valerie M. Crowley & Susana G. Campino & April E. Williams & Laura G. Drought & Dominic P. Kwiatkowski & David A. Bake, 2014. "A transcriptional switch underlies commitment to sexual development in malaria parasites," Nature, Nature, vol. 507(7491), pages 248-252, March.
    4. Irene Otero-Muras & Julio R Banga & Antonio A Alonso, 2012. "Characterizing Multistationarity Regimes in Biochemical Reaction Networks," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-12, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Silvia Berra & Alessandro Torraca & Federico Benvenuto & Sara Sommariva, 2024. "Combined Newton-Gradient Method for Constrained Root-Finding in Chemical Reaction Networks," Journal of Optimization Theory and Applications, Springer, vol. 200(1), pages 404-427, January.
    2. N. C. Pati, 2023. "Bifurcations and multistability in a physically extended Lorenz system for rotating convection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(8), pages 1-15, August.
    3. Antonio A. Alonso & Irene Otero-Muras & Manuel Pájaro, 2018. "Routes to Multiple Equilibria for Mass-Action Kinetic Systems," Complexity, Hindawi, vol. 2018, pages 1-13, December.

    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. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    2. João F Matias Rodrigues & Andreas Wagner, 2009. "Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-11, December.
    3. Yann S Dufour & Sébastien Gillet & Nicholas W Frankel & Douglas B Weibel & Thierry Emonet, 2016. "Direct Correlation between Motile Behavior and Protein Abundance in Single Cells," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-25, September.
    4. Gheorghe Maria & Cristiana Luminiţa Gîjiu & Cristina Maria & Carmen Tociu, 2018. "Importance of Considering the Isotonic System Hypothesis When Modelling the Self-Control of Gene Expression Regulatory Modules in Living Cells," Current Trends in Biomedical Engineering & Biosciences, Juniper Publishers Inc., vol. 12(2), pages 29-48, February.
    5. Antonio A. Alonso & Irene Otero-Muras & Manuel Pájaro, 2018. "Routes to Multiple Equilibria for Mass-Action Kinetic Systems," Complexity, Hindawi, vol. 2018, pages 1-13, December.
    6. Timo R Maarleveld & Meike T Wortel & Brett G Olivier & Bas Teusink & Frank J Bruggeman, 2015. "Interplay between Constraints, Objectives, and Optimality for Genome-Scale Stoichiometric Models," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-21, April.
    7. Hannesson, Erik & Sellers, Jordan & Walker, Ethan & Webb, Benjamin, 2022. "Network specialization: A topological mechanism for the emergence of cluster synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    8. Saka, Yasushi & MacPherson, Murray & Giuraniuc, Claudiu V., 2017. "Generation and precise control of dynamic biochemical gradients for cellular assays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 132-145.
    9. Mirja Meyer & Marc-Thorsten Hütt & Julia Bendul, 2016. "The elementary flux modes of a manufacturing system: a novel approach to explore the relationship of network structure and function," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4145-4160, July.

    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:pcbi00:1005454. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.