IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1008051.html
   My bibliography  Save this article

Quorum sensing via dynamic cytokine signaling comprehensively explains divergent patterns of effector choice among helper T cells

Author

Listed:
  • Edward C Schrom II
  • Simon A Levin
  • Andrea L Graham

Abstract

In the animal kingdom, various forms of swarming enable groups of autonomous individuals to transform uncertain information into unified decisions which are probabilistically beneficial. Crossing scales from individual to group decisions requires dynamically accumulating signals among individuals. In striking parallel, the mammalian immune system is also a group of decentralized autonomous units (i.e. cells) which collectively navigate uncertainty with the help of dynamically accumulating signals (i.e. cytokines). Therefore, we apply techniques of understanding swarm behavior to a decision-making problem in the mammalian immune system, namely effector choice among CD4+ T helper (Th) cells. We find that incorporating dynamic cytokine signaling into a simple model of Th differentiation comprehensively explains divergent observations of this process. The plasticity and heterogeneity of individual Th cells, the tunable mixtures of effector types that can be generated in vitro, and the polarized yet updateable group effector commitment often observed in vivo are all explained by the same set of underlying molecular rules. These rules reveal that Th cells harness dynamic cytokine signaling to implement a system of quorum sensing. Quorum sensing, in turn, may confer adaptive advantages on the mammalian immune system, especially during coinfection and during coevolution with manipulative parasites. This highlights a new way of understanding the mammalian immune system as a cellular swarm, and it underscores the power of collectives throughout nature.Author summary: Across the animal kingdom, swarming is a common phenomenon by which many autonomous individuals act as a unified group. Similarly, helper T cells in the mammalian immune system are numerous and autonomous, and yet they collectively make important decisions, such as which immune weapons to recruit during a given infection (i.e. “effector choice”). However, due to varying experimental results, it is unclear when, how, and why helper T cells coordinate unified effector choices. Inspired by studies of swarms in the animal kingdom, we answer all three questions with a single set of simple mathematical rules governing the interactions of individual cells. Helper T cells engage in quorum sensing, transitioning from mixed to unified group decisions only at high cell densities. Quorum sensing emerges naturally from the interplay between molecular circuits within helper T cells and dynamically accumulating signals between helper T cells. Quorum sensing may have evolved because it helps our immune systems discern legitimate changes in effector needs from parasitic sabotage of the effector choice system. These insights demonstrate that the quantitative study of swarm biology can shed new light on the organization and function of the mammalian immune system.

Suggested Citation

  • Edward C Schrom II & Simon A Levin & Andrea L Graham, 2020. "Quorum sensing via dynamic cytokine signaling comprehensively explains divergent patterns of effector choice among helper T cells," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-21, July.
  • Handle: RePEc:plo:pcbi00:1008051
    DOI: 10.1371/journal.pcbi.1008051
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008051
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008051&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1008051?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. Alex Sigal & Ron Milo & Ariel Cohen & Naama Geva-Zatorsky & Yael Klein & Yuvalal Liron & Nitzan Rosenfeld & Tamar Danon & Natalie Perzov & Uri Alon, 2006. "Variability and memory of protein levels in human cells," Nature, Nature, vol. 444(7119), pages 643-646, 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. Tae J Lee & Jeffrey Wong & Sena Bae & Anna Jisu Lee & Allison Lopatkin & Fan Yuan & Lingchong You, 2015. "A Power-Law Dependence of Bacterial Invasion on Mammalian Host Receptors," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-17, April.
    2. Kazunari Iwamoto & Yuki Shindo & Koichi Takahashi, 2016. "Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-18, November.
    3. UnJin Lee & John J Skinner & John Reinitz & Marsha Rich Rosner & Eun-Jin Kim, 2015. "Noise-Driven Phenotypic Heterogeneity with Finite Correlation Time in Clonal Populations," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
    4. David A Sivak & Matt Thomson, 2014. "Environmental Statistics and Optimal Regulation," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-12, September.
    5. Ming Ni & Antoine L Decrulle & Fanette Fontaine & Alice Demarez & Francois Taddei & Ariel B Lindner, 2012. "Pre-Disposition and Epigenetics Govern Variation in Bacterial Survival upon Stress," PLOS Genetics, Public Library of Science, vol. 8(12), pages 1-11, December.
    6. Monica T. Dayao & Maigan Brusko & Clive Wasserfall & Ziv Bar-Joseph, 2022. "Membrane marker selection for segmenting single cell spatial proteomics data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    7. Tanya L Leise & Connie W Wang & Paula J Gitis & David K Welsh, 2012. "Persistent Cell-Autonomous Circadian Oscillations in Fibroblasts Revealed by Six-Week Single-Cell Imaging of PER2::LUC Bioluminescence," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
    8. Liang Qiao & Robert B Nachbar & Ioannis G Kevrekidis & Stanislav Y Shvartsman, 2007. "Bistability and Oscillations in the Huang-Ferrell Model of MAPK Signaling," PLOS Computational Biology, Public Library of Science, vol. 3(9), pages 1-8, September.
    9. Yelyzaveta Shlyakhtina & Bianca Bloechl & Maximiliano M. Portal, 2023. "BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    10. Anissa Guillemin & Ronan Duchesne & Fabien Crauste & Sandrine Gonin-Giraud & Olivier Gandrillon, 2019. "Drugs modulating stochastic gene expression affect the erythroid differentiation process," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-19, November.
    11. Steven A Frank, 2013. "Evolution of Robustness and Cellular Stochasticity of Gene Expression," PLOS Biology, Public Library of Science, vol. 11(6), pages 1-3, June.
    12. Alok Maity & Roy Wollman, 2020. "Information transmission from NFkB signaling dynamics to gene expression," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-16, August.
    13. Warren Pilbrough & Trent P Munro & Peter Gray, 2009. "Intraclonal Protein Expression Heterogeneity in Recombinant CHO Cells," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-11, December.
    14. Suzanne Gaudet & Sabrina L Spencer & William W Chen & Peter K Sorger, 2012. "Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-15, April.
    15. Hui Zhang & Yueling Chen & Yong Chen, 2012. "Noise Propagation in Gene Regulation Networks Involving Interlinked Positive and Negative Feedback Loops," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.

    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:pcbi00:1008051. 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.