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Stable maintenance of hidden switches as a strategy to increase the gene expression stability

Nature Computational Science volume 1, pages 62–70 (2021)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

In response to severe genetic and environmental perturbations, wild-type organisms can express hidden alternative phenotypes adaptive to such adverse conditions. While our theoretical understanding of the population-level fitness advantage and evolution of phenotypic switching under variable environments has grown, the mechanism by which these organisms maintain phenotypic switching capabilities under static environments remains to be elucidated. Here, using computational simulations, we analyzed the evolution of gene circuits under natural selection and found that different strategies evolved to increase the gene expression stability near the optimum level. In a population comprising bistable individuals, a strategy of maintaining bistability and raising the potential barrier separating the bistable regimes was consistently taken. Our results serve as evidence that hidden bistable switches can be stably maintained during environmental stasis—an essential property enabling the timely release of adaptive alternatives with small genetic changes in the event of substantial perturbations.

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Fig. 1: Comparison of genotypic characteristics among different phases.
Fig. 2: Association of bistable populations from different phases.
Fig. 3: Evolution of gene expression stability.
Fig. 4: Evolutionary simulation results of Gaussian-based models.

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Data availability

The simulation data along with the data analysis scripts associated with the current submission and the source data for Figs. 1–4 are available in the Zenodo repository48.

Code availability

The simulator source code and data analysis scripts have been deposited in GitHub, and they can be accessed at https://github.com/hkuwahara/evo-hidden-switch. The tool package has also been deposited in Zenodo48.

References

  1. Waddington, C. H. Canalization of development and the inheritance of acquired characters. Nature 150, 563–565 (1942).

    Article  Google Scholar 

  2. Waddington, C. H. Genetic assimilation of an acquired character. Evolution 7, 118–126 (1953).

    Article  Google Scholar 

  3. Schmalhausen I. I., Dordick I. & Dobzhansky T. Factors of Evolution: The Theory of Stabilizing Selection (Univ. Chicago Press, 1987).

  4. Gibson, G. & Wagner, G. Canalization in evolutionary genetics: a stabilizing theory? Bioessays 22, 372–380 (2000).

    Article  Google Scholar 

  5. West-Eberhard, M. J. Toward a modern revival of Darwin’s theory of evolutionary novelty. Phil. Sci. 75, 899–908 (2008).

    Article  Google Scholar 

  6. Félix, M. A. & Barkoulas, M. Pervasive robustness in biological systems. Nat. Rev. Genet. 16, 483–496 (2015).

    Article  Google Scholar 

  7. West-Eberhard, M. J. Phenotypic plasticity and the origins of diversity. Annu. Rev. Ecol. Syst. 20, 249–278 (1989).

    Article  Google Scholar 

  8. Suzuki, Y. & Nijhout, H. F. Evolution of a polyphenism by genetic accommodation. Science 311, 650–652 (2006).

    Article  Google Scholar 

  9. Eldar, A. et al. Partial penetrance facilitates developmental evolution in bacteria. Nature 460, 510–514 (2009).

    Article  Google Scholar 

  10. Raj, A., Rifkin, S. A., Andersen, E. & van Oudenaarden, A. Variability in gene expression underlies incomplete penetrance. Nature 463, 913–918 (2010).

    Article  Google Scholar 

  11. Specchia, V. et al. Hsp90 prevents phenotypic variation by suppressing the mutagenic activity of transposons. Nature 463, 662–665 (2010).

    Article  Google Scholar 

  12. West-Eberhard, M. J. Developmental Plasticity and Evolution (Oxford Univ. Press, 2003).

  13. Masel, J., King, O. D. & Maughan, H. The loss of adaptive plasticity during long periods of environmental stasis. Am. Nat. 169, 38–46 (2007).

    Article  Google Scholar 

  14. Kærn, M., Elston, T., Blake, W. & Collins, J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

    Article  Google Scholar 

  15. Raser, J. M. & O’Shea, E. K. Noise in gene expression: origins, consequences, and control. Science 309, 2010–2013 (2005).

    Article  Google Scholar 

  16. Losick, R. & Desplan, C. Stochasticity and cell fate. Science 320, 65–68 (2008).

    Article  Google Scholar 

  17. Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    Article  Google Scholar 

  18. Acar, M., Mettetal, J. T. & van Oudenaarden, A. Stochastic switching as a survival strategy in fluctuating environments. Nat. Genet. 40, 471–475 (2008).

    Article  Google Scholar 

  19. Capp, J. P. Noise-driven heterogeneity in the rate of genetic-variant generation as a basis for evolvability. Genetics 185, 395–404 (2010).

    Article  Google Scholar 

  20. Johnston, R. J. Jr & Desplan, C. Stochastic mechanisms of cell fate specification that yield random or robust outcomes. Annu. Rev. Cell Dev. Biol. 26, 689–719 (2010).

    Article  Google Scholar 

  21. Kuwahara, H. & Soyer, O. S. Bistability in feedback circuits as a byproduct of evolution of evolvability. Mol. Syst. Biol. 8, 564 (2012).

    Article  Google Scholar 

  22. Becskei, A., Séraphin, B. & Serrano, L. Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J. 20, 2528–2535 (2001).

    Article  Google Scholar 

  23. Thattai, M. & Van Oudenaarden, A. Stochastic gene expression in fluctuating environments. Genetics 167, 523–530 (2004).

    Article  Google Scholar 

  24. Salathé, M., Van Cleve, J. & Feldman, M. W. Evolution of stochastic switching rates in asymmetric fitness landscapes. Genetics 182, 1159–1164 (2009).

    Article  Google Scholar 

  25. Balázsi, G., van Oudenaarden, A. & Collins, J. J. Cellular decision making and biological noise: from microbes to mammals. Cell 144, 910–925 (2011).

    Article  Google Scholar 

  26. Liberman, U., Van Cleve, J. & Feldman, M. W. On the evolution of mutation in changing environments: recombination and phenotypic switching. Genetics 187, 837–851 (2011).

    Article  Google Scholar 

  27. Bedford, T. & Hartl, D. L. Optimization of gene expression by natural selection. Proc. Natl Acad. Sci. USA 106, 1133–1138 (2009).

    Article  Google Scholar 

  28. Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D. & van Oudenaarden, A. Regulation of noise in the expression of a single gene. Nat. Genet. 31, 69–73 (2002).

    Article  Google Scholar 

  29. Lachmann, M. & Jablonka, E. The inheritance of phenotypes: an adaptation to fluctuating environments. J. Theor. Biol. 181, 1–9 (1996).

    Article  Google Scholar 

  30. Meyers, L. A., Ancel, F. D. & Lachmann, M. Evolution of genetic potential. PLoS Comput. Biol. 1, 236–243. (2005).

    Google Scholar 

  31. Zhang, Z., Qian, W. & Zhang, J. Positive selection for elevated gene expression noise in yeast. Mol. Syst. Biol. 5, 299 (2009).

    Article  Google Scholar 

  32. Bintu, L. et al. Transcriptional regulation by the numbers: models. Curr. Opin. Genet. Dev. 15, 116–124 (2005).

    Article  Google Scholar 

  33. Kuwahara, H. et al. in Transactions on Computational Systems Biology VI (eds Priami, C. & Plotkin, G.) 150–175 (Springer, 2006).

  34. Gunawardena, J. Time-scale separation—Michaelis and Menten’s old idea, still bearing fruit. FEBS J. 281, 473–488 (2014).

    Article  Google Scholar 

  35. Acar, M., Becskei, A. & van Oudenaarden, A. Enhancement of cellular memory by reducing stochastic transitions. Nature 435, 228–232 (2005).

    Article  Google Scholar 

  36. Xiong, W. & Ferrell, J. E. Jr A positive-feedback-based bistable’memory module’ that governs a cell fate decision. Nature 426, 460–465 (2003).

    Article  Google Scholar 

  37. Lestas, I., Vinnicombe, G. & Paulsson, J. Fundamental limits on the suppression of molecular fluctuations. Nature 467, 174–178 (2010).

    Article  Google Scholar 

  38. Thattai, M. & van Oudenaarden, A. Intrinsic noise in gene regulatory networks. Proc. Natl Acad. Sci. USA 98, 8614–8619 (2001).

    Article  Google Scholar 

  39. Kuwahara, H., Arold, S. T. & Gao, X. Beyond initiation-limited translational bursting: the effects of burst size distributions on the stability of gene expression. Integr. Biol. 7, 1622–1632 (2015).

    Article  Google Scholar 

  40. Duveau, F. et al. Fitness effects of altering gene expression noise in Saccharomyces cerevisiae. eLife 7, e37272 (2018).

    Article  Google Scholar 

  41. Gibson, G. & Dworkin, I. Uncovering cryptic genetic variation. Nat. Rev. Genet. 5, 681–690 (2004).

    Article  Google Scholar 

  42. Nevozhay, D., Adams, R. M., Van Itallie, E., Bennett, M. R. & Balázsi, G. Mapping the environmental fitness landscape of a synthetic gene circuit. PLoS Comput. Biol. 8, e1002480 (2012).

    Article  Google Scholar 

  43. González, C. et al. Stress-response balance drives the evolution of a network module and its host genome. Mol. Syst. Biol. 11, 827 (2015).

    Article  Google Scholar 

  44. Kheir Gouda, M., Manhart, M. & Balázsi, G. Evolutionary regain of lost gene circuit function. Proc. Natl Acad. Sci. USA 116, 25162–25171 (2019).

    Article  Google Scholar 

  45. Pujadas, E. & Feinberg, A. P. Regulated noise in the epigenetic landscape of development and disease. Cell 148, 1123–1131 (2012).

    Article  Google Scholar 

  46. Gillespie, D. T. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977).

    Article  Google Scholar 

  47. Friedman, N., Cai, L. & Xie, X. S. Linking stochastic dynamics to population distribution: an analytical framework of gene expression. Phys. Rev. Lett. 97, 168302 (2006).

    Article  Google Scholar 

  48. Kuwahara, H. Simulation tool and data analysis scripts for the study of stable maintenance of bistable switch under static environments (version 0.16). Zenodo https://doi.org/10.5281/zenodo.4120179 (2020).

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Acknowledgements

We thank O. Soyer and T. Gojobori for their comments on an earlier version of the manuscript. X.G. was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under award numbers BAS/1/1624-01, URF/1/3412-01, URF/1/3450-01, FCC/1/1976-18, FCC/1/1976-23, FCC/1/1976-25, FCC/1/1976-26 and FCS/1/4102-02.

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Authors and Affiliations

  1. Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Hiroyuki Kuwahara & Xin Gao

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  1. Hiroyuki Kuwahara
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  2. Xin Gao
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Contributions

H.K. conceived and designed the study, developed tools, performed analysis and wrote the paper. X.G. oversaw the project and wrote the paper. All authors reviewed the final manuscript.

Corresponding author

Correspondence to Xin Gao.

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The authors declare no competing interests.

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Peer review information Nature Computational Science thanks Gábor Balázsi and Xiaojun Tian for their contribution to the peer review of this work. Fernando Chirigati was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary Information

Supplementary Sections 1–4 and Figs. 1–18.

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Kuwahara, H., Gao, X. Stable maintenance of hidden switches as a strategy to increase the gene expression stability. Nat Comput Sci 1, 62–70 (2021). https://doi.org/10.1038/s43588-020-00001-y

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