IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/0050252.html
   My bibliography  Save this article

Negative Autoregulation by FAS Mediates Robust Fetal Erythropoiesis

Author

Listed:
  • Merav Socolovsky
  • Michael Murrell
  • Ying Liu
  • Ramona Pop
  • Ermelinda Porpiglia
  • Andre Levchenko

Abstract

Tissue development is regulated by signaling networks that control developmental rate and determine ultimate tissue mass. Here we present a novel computational algorithm used to identify regulatory feedback and feedforward interactions between progenitors in developing erythroid tissue. The algorithm makes use of dynamic measurements of red cell progenitors between embryonic days 12 and 15 in the mouse. It selects for intercellular interactions that reproduce the erythroid developmental process and endow it with robustness to external perturbations. This analysis predicts that negative autoregulatory interactions arise between early erythroblasts of similar maturation stage. By studying embryos mutant for the death receptor FAS, or for its ligand, FASL, and by measuring the rate of FAS-mediated apoptosis in vivo, we show that FAS and FASL are pivotal negative regulators of fetal erythropoiesis, in the manner predicted by the computational model. We suggest that apoptosis in erythroid development mediates robust homeostasis regulating the number of red blood cells reaching maturity. : The factors that control the rate of tissue growth during development are largely unknown. During embryogenesis, the formation of anucleated red blood cells (erythropoiesis) begins in the liver, with a dramatic expansion in erythropoietic tissue mass, occurring ten times faster than overall embryonic growth. We hypothesized that a network of cell–cell interactions within the erythroid microenvironment regulates this growth burst. To identify these regulatory interactions, we made use of the empirical finding that developmental processes are relatively robust to environmental perturbations. We determined how the frequency of erythroid progenitors in each of four sequential differentiation states varies during early development in vivo. We then modeled this behavior, and computationally selected those interactions that endow the network with resistance to external perturbations. This analysis predicted that erythroblasts in “state 2” of differentiation negatively regulate each other. We found that this autoregulatory interaction is mediated by the death receptor FAS and its ligand, FASL, which are co-expressed in state 2 cells. FAS-mediated cell death occurs only when the frequency of state 2 cells is high enough to permit their sufficient proximity. In this manner, FAS-mediated apoptosis dampens the initially rapid expansion of state 2 cells, and buffers unexpected fluctuations in their number, contributing to the system's robustness. We propose that a similar approach could be used to identify intercellular interactions in other rapidly growing tissues. A novel computational algorithm is used to identify regulatory feedback and feedforward interactions between progenitors in developing erythroid tissue.

Suggested Citation

  • Merav Socolovsky & Michael Murrell & Ying Liu & Ramona Pop & Ermelinda Porpiglia & Andre Levchenko, 2007. "Negative Autoregulation by FAS Mediates Robust Fetal Erythropoiesis," PLOS Biology, Public Library of Science, vol. 5(10), pages 1-16, September.
  • Handle: RePEc:plo:pbio00:0050252
    DOI: 10.1371/journal.pbio.0050252
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0050252
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.0050252&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.0050252?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. George von Dassow & Eli Meir & Edwin M. Munro & Garrett M. Odell, 2000. "The segment polarity network is a robust developmental module," Nature, Nature, vol. 406(6792), pages 188-192, July.
    2. Matthew Freeman, 2000. "Feedback control of intercellular signalling in development," Nature, Nature, vol. 408(6810), pages 313-319, November.
    3. Avigdor Eldar & Ruslan Dorfman & Daniel Weiss & Hilary Ashe & Ben-Zion Shilo & Naama Barkai, 2002. "Robustness of the BMP morphogen gradient in Drosophila embryonic patterning," Nature, Nature, vol. 419(6904), pages 304-308, September.
    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. Ermelinda Porpiglia & Daniel Hidalgo & Miroslav Koulnis & Abraham R Tzafriri & Merav Socolovsky, 2012. "Stat5 Signaling Specifies Basal versus Stress Erythropoietic Responses through Distinct Binary and Graded Dynamic Modalities," PLOS Biology, Public Library of Science, vol. 10(8), pages 1-19, August.

    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. Stefano Ciliberti & Olivier C Martin & Andreas Wagner, 2007. "Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology," PLOS Computational Biology, Public Library of Science, vol. 3(2), pages 1-10, February.
    2. Adel Dayarian & Madalena Chaves & Eduardo D Sontag & Anirvan M Sengupta, 2009. "Shape, Size, and Robustness: Feasible Regions in the Parameter Space of Biochemical Networks," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-12, January.
    3. Cummings, F.W, 2004. "A model of morphogenesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 339(3), pages 531-547.
    4. Ryan N Gutenkunst & Joshua J Waterfall & Fergal P Casey & Kevin S Brown & Christopher R Myers & James P Sethna, 2007. "Universally Sloppy Parameter Sensitivities in Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-8, October.
    5. Mingzhu Sun & Hui Xu & Xingjuan Zeng & Xin Zhao, 2017. "Automated numerical simulation of biological pattern formation based on visual feedback simulation framework," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-16, February.
    6. Zeina Shreif & Vipul Periwal, 2014. "A Network Characteristic That Correlates Environmental and Genetic Robustness," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-23, February.
    7. Zhou, Peipei & Cai, Shuiming & Liu, Zengrong & Chen, Luonan & Wang, Ruiqi, 2013. "Coupling switches and oscillators as a means to shape cellular signals in biomolecular systems," Chaos, Solitons & Fractals, Elsevier, vol. 50(C), pages 115-126.
    8. Andreas Wagner, 2015. "Causal Drift, Robust Signaling, and Complex Disease," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-29, March.
    9. Manuel Cambón & Óscar Sánchez, 2022. "Thermodynamic Modelling of Transcriptional Control: A Sensitivity Analysis," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
    10. Debasish Mondal & Edward Dougherty & Abhishek Mukhopadhyay & Adria Carbo & Guang Yao & Jianhua Xing, 2014. "Systematic Reverse Engineering of Network Topologies: A Case Study of Resettable Bistable Cellular Responses," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
    11. Jens Grauer & Falko Schmidt & Jesús Pineda & Benjamin Midtvedt & Hartmut Löwen & Giovanni Volpe & Benno Liebchen, 2021. "Active droploids," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    12. Guillermo Rodrigo & Santiago F Elena, 2011. "Structural Discrimination of Robustness in Transcriptional Feedforward Loops for Pattern Formation," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
    13. Miles Miller & Marc Hafner & Eduardo Sontag & Noah Davidsohn & Sairam Subramanian & Priscilla E M Purnick & Douglas Lauffenburger & Ron Weiss, 2012. "Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-18, July.
    14. Sriram, K., 2006. "Effects of positive electrical feedback in the oscillating Belousov–Zhabotinsky reaction: Experiments and simulations," Chaos, Solitons & Fractals, Elsevier, vol. 28(4), pages 1055-1066.
    15. Rutger Hermsen & Bas Ursem & Pieter Rein ten Wolde, 2010. "Combinatorial Gene Regulation Using Auto-Regulation," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-13, June.
    16. Carl Song & Hilary Phenix & Vida Abedi & Matthew Scott & Brian P Ingalls & Mads Kærn & Theodore J Perkins, 2010. "Estimating the Stochastic Bifurcation Structure of Cellular Networks," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-11, March.
    17. Ariel L Rivas & Mark D Jankowski & Renata Piccinini & Gabriel Leitner & Daniel Schwarz & Kevin L Anderson & Jeanne M Fair & Almira L Hoogesteijn & Wilfried Wolter & Marcelo Chaffer & Shlomo Blum & Tom, 2013. "Feedback-Based, System-Level Properties of Vertebrate-Microbial Interactions," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-16, February.
    18. 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.
    19. Robyn P. Araujo & Lance A. Liotta, 2023. "Universal structures for adaptation in biochemical reaction networks," Nature Communications, Nature, vol. 14(1), pages 1-12, 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:pbio00:0050252. 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.