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Regulated cell-to-cell variation in a cell-fate decision system

Author

Listed:
  • Alejandro Colman-Lerner

    (The Molecular Sciences Institute)

  • Andrew Gordon

    (The Molecular Sciences Institute)

  • Eduard Serra

    (The Molecular Sciences Institute)

  • Tina Chin

    (The Molecular Sciences Institute)

  • Orna Resnekov

    (The Molecular Sciences Institute)

  • Drew Endy

    (Massachusetts Institute of Technology)

  • C. Gustavo Pesce

    (The Molecular Sciences Institute)

  • Roger Brent

    (The Molecular Sciences Institute)

Abstract

Here we studied the quantitative behaviour and cell-to-cell variability of a prototypical eukaryotic cell-fate decision system, the mating pheromone response pathway in yeast. We dissected and measured sources of variation in system output, analysing thousands of individual, genetically identical cells. Only a small proportion of total cell-to-cell variation is caused by random fluctuations in gene transcription and translation during the response (‘expression noise’). Instead, variation is dominated by differences in the capacity of individual cells to transmit signals through the pathway (‘pathway capacity’) and to express proteins from genes (‘expression capacity’). Cells with high expression capacity express proteins at a higher rate and increase in volume more rapidly. Our results identify two mechanisms that regulate cell-to-cell variation in pathway capacity. First, the MAP kinase Fus3 suppresses variation at high pheromone levels, while the MAP kinase Kss1 enhances variation at low pheromone levels. Second, pathway capacity and expression capacity are negatively correlated, suggesting a compensatory mechanism that allows cells to respond more precisely to pheromone in the presence of a large variation in expression capacity.

Suggested Citation

  • Alejandro Colman-Lerner & Andrew Gordon & Eduard Serra & Tina Chin & Orna Resnekov & Drew Endy & C. Gustavo Pesce & Roger Brent, 2005. "Regulated cell-to-cell variation in a cell-fate decision system," Nature, Nature, vol. 437(7059), pages 699-706, September.
  • Handle: RePEc:nat:nature:v:437:y:2005:i:7059:d:10.1038_nature03998
    DOI: 10.1038/nature03998
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    Citations

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    Cited by:

    1. Bar Haim Y. & Booth James G. & Wells Martin T., 2012. "A Mixture-Model Approach for Parallel Testing for Unequal Variances," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-21, January.
    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. Christoph Zechner & Heinz Koeppl, 2014. "Uncoupled Analysis of Stochastic Reaction Networks in Fluctuating Environments," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-9, December.
    4. Artémis Llamosi & Andres M Gonzalez-Vargas & Cristian Versari & Eugenio Cinquemani & Giancarlo Ferrari-Trecate & Pascal Hersen & Gregory Batt, 2016. "What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.
    5. 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.
    6. Michael Chevalier & Ophelia Venturelli & Hana El-Samad, 2015. "The Impact of Different Sources of Fluctuations on Mutual Information in Biochemical Networks," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-21, October.
    7. Bryan Sands & Soo Yun & Alexander R. Mendenhall, 2021. "Introns control stochastic allele expression bias," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    8. Steven S Andrews & Nathan J Addy & Roger Brent & Adam P Arkin, 2010. "Detailed Simulations of Cell Biology with Smoldyn 2.1," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-10, March.

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