IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/0050239.html
   My bibliography  Save this article

Heritable Stochastic Switching Revealed by Single-Cell Genealogy

Author

Listed:
  • Benjamin B Kaufmann
  • Qiong Yang
  • Jerome T Mettetal
  • Alexander van Oudenaarden

Abstract

The partitioning and subsequent inheritance of cellular factors like proteins and RNAs is a ubiquitous feature of cell division. However, direct quantitative measures of how such nongenetic inheritance affects subsequent changes in gene expression have been lacking. We tracked families of the yeast Saccharomyces cerevisiae as they switch between two semi-stable epigenetic states. We found that long after two cells have divided, they continued to switch in a synchronized manner, whereas individual cells have exponentially distributed switching times. By comparing these results to a Poisson process, we show that the time evolution of an epigenetic state depends initially on inherited factors, with stochastic processes requiring several generations to decorrelate closely related cells. Finally, a simple stochastic model demonstrates that a single fluctuating regulatory protein that is synthesized in large bursts can explain the bulk of our results. : When cells divide, not only DNA but an entire pattern of gene expression can be passed from mother to daughter cell. Once cell division is complete, random processes cause this pattern to change, with closely related cells growing less similar over time. We measured inheritance of a dynamic gene-expression state in single yeast cells. We used an engineered network where individual cells switch between two semi-stable states (ON and OFF), even in a constant environment. Several generations after cells have physically separated, many pairs of closely related cells switch in near synchrony. We quantified this effect by measuring how likely a mother cell is to have switched given that the daughter cell has already switched. This yields a conditional probability distribution that is very different from the exponential one found in the entire population of switching cells. We measured the extent to which this correlation between switching cells persists by comparing our results with a model Poisson process. Together, these findings demonstrate the inheritance of a dynamic gene expression state whose post-division changes include both random factors arising from noise as well as correlated factors that originate in two related cells' shared history. Finally, we constructed a model that demonstrates that our major findings can be explained by burst-like fluctuations in the levels of a single regulatory protein. When cells divide, each daughter cell inherits a share of the contents of the mother. If the contents include a regulatory system with a feedback loop, sister cells switch states in synchrony.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pbio00:0050239
    DOI: 10.1371/journal.pbio.0050239
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0050239
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.0050239&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.0050239?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. John R. S. Newman & Sina Ghaemmaghami & Jan Ihmels & David K. Breslow & Matthew Noble & Joseph L. DeRisi & Jonathan S. Weissman, 2006. "Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise," Nature, Nature, vol. 441(7095), pages 840-846, June.
    3. Gürol M. Süel & Jordi Garcia-Ojalvo & Louisa M. Liberman & Michael B. Elowitz, 2006. "An excitable gene regulatory circuit induces transient cellular differentiation," Nature, Nature, vol. 440(7083), pages 545-550, March.
    4. Long Cai & Nir Friedman & X. Sunney Xie, 2006. "Stochastic protein expression in individual cells at the single molecule level," Nature, Nature, vol. 440(7082), pages 358-362, March.
    5. Dmitri Volfson & Jennifer Marciniak & William J. Blake & Natalie Ostroff & Lev S. Tsimring & Jeff Hasty, 2006. "Origins of extrinsic variability in eukaryotic gene expression," Nature, Nature, vol. 439(7078), pages 861-864, February.
    6. Johan Paulsson, 2004. "Summing up the noise in gene networks," Nature, Nature, vol. 427(6973), pages 415-418, January.
    7. 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.
    8. Ertugrul M. Ozbudak & Mukund Thattai & Han N. Lim & Boris I. Shraiman & Alexander van Oudenaarden, 2004. "Multistability in the lactose utilization network of Escherichia coli," Nature, Nature, vol. 427(6976), pages 737-740, February.
    9. Murat Acar & Attila Becskei & Alexander van Oudenaarden, 2005. "Enhancement of cellular memory by reducing stochastic transitions," Nature, Nature, vol. 435(7039), pages 228-232, May.
    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. 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.
    2. 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.
    3. Gary Friedman & Stephen McCarthy & Dmitrii Rachinskii, 2014. "Hysteresis Can Grant Fitness in Stochastically Varying Environment," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-9, July.

    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. Arjun Raj & Charles S Peskin & Daniel Tranchina & Diana Y Vargas & Sanjay Tyagi, 2006. "Stochastic mRNA Synthesis in Mammalian Cells," PLOS Biology, Public Library of Science, vol. 4(10), pages 1-13, September.
    2. 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.
    3. Lee, Julian, 2023. "Poisson distributions in stochastic dynamics of gene expression: What events do they count?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    4. Matthieu Wyart & David Botstein & Ned S Wingreen, 2010. "Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-14, November.
    5. Najme Khorasani & Mehdi Sadeghi & Abbas Nowzari-Dalini, 2020. "A computational model of stem cell molecular mechanism to maintain tissue homeostasis," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-25, July.
    6. 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.
    7. 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.
    8. Chen, Aimin & Tian, Tianhai & Chen, Yiren & Zhou, Tianshou, 2022. "Stochastic analysis of a complex gene-expression model," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    9. Abhyudai Singh & Mohammad Soltani, 2013. "Quantifying Intrinsic and Extrinsic Variability in Stochastic Gene Expression Models," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    10. Mayu Sugiyama & Takashi Saitou & Hiroshi Kurokawa & Asako Sakaue-Sawano & Takeshi Imamura & Atsushi Miyawaki & Tadahiro Iimura, 2014. "Live Imaging-Based Model Selection Reveals Periodic Regulation of the Stochastic G1/S Phase Transition in Vertebrate Axial Development," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-16, December.
    11. Stuart Aitken & Marie-Cécile Robert & Ross D Alexander & Igor Goryanin & Edouard Bertrand & Jean D Beggs, 2010. "Processivity and Coupling in Messenger RNA Transcription," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-12, January.
    12. Jérémie Bourdon & Damien Eveillard & Anne Siegel, 2011. "Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network," PLOS Computational Biology, Public Library of Science, vol. 7(9), pages 1-11, September.
    13. Ruoyu Luo & Lin Ye & Chenyang Tao & Kankan Wang, 2013. "Simulation of E. coli Gene Regulation including Overlapping Cell Cycles, Growth, Division, Time Delays and Noise," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-10, April.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Saurabh Modi & Supravat Dey & Abhyudai Singh, 2021. "Noise suppression in stochastic genetic circuits using PID controllers," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-25, July.
    19. Karl P. Gerhardt & Satyajit D. Rao & Evan J. Olson & Oleg A. Igoshin & Jeffrey J. Tabor, 2021. "Independent control of mean and noise by convolution of gene expression distributions," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    20. Hao Ge & Pingping Wu & Hong Qian & Xiaoliang Sunney Xie, 2018. "Relatively slow stochastic gene-state switching in the presence of positive feedback significantly broadens the region of bimodality through stabilizing the uninduced phenotypic state," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-24, March.

    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:pbio00:0050239. 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.