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Matsumoto T, Mineta K, Osada N, et al. An Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous. Front Genet. 2015;6:336doi: 10.3389/fgene.2015.00336.
Matsumoto, T., Mineta, K., Osada, N., & Araki, H. (2015). An Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous. Frontiers in genetics, 6336. https://doi.org/10.3389/fgene.2015.00336
Matsumoto, Tomotaka, et al. "An Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous." Frontiers in genetics vol. 6 (2015): 336. doi: https://doi.org/10.3389/fgene.2015.00336
Matsumoto T, Mineta K, Osada N, Araki H. An Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous. Front Genet. 2015 Nov 24;6:336. doi: 10.3389/fgene.2015.00336. eCollection 2015. PMID: 26635872; PMCID: PMC4656826.
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Front Genet. 2015 Nov 24;6:336. doi: 10.3389/fgene.2015.00336. eCollection 2015.

An Individual-Based Diploid Model Predicts Limited Conditions Under Which Stochastic Gene Expression Becomes Advantageous.

Frontiers in genetics

Tomotaka Matsumoto, Katsuhiko Mineta, Naoki Osada, Hitoshi Araki

Affiliations

  1. Graduate School of Systems Life Sciences, Kyushu University Fukuoka, Japan ; Department of Population Genetics, National Institute of Genetics Mishima, Japan.
  2. Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia.
  3. Department of Population Genetics, National Institute of Genetics Mishima, Japan ; Department of Genetics, SOKENDAI (The Graduate University for Advanced Studies) Mishima, Japan.
  4. Research Faculty of Agriculture, Hokkaido University Sapporo, Japan.

PMID: 26635872 PMCID: PMC4656826 DOI: 10.3389/fgene.2015.00336

Abstract

Recent studies suggest the existence of a stochasticity in gene expression (SGE) in many organisms, and its non-negligible effect on their phenotype and fitness. To date, however, how SGE affects the key parameters of population genetics are not well understood. SGE can increase the phenotypic variation and act as a load for individuals, if they are at the adaptive optimum in a stable environment. On the other hand, part of the phenotypic variation caused by SGE might become advantageous if individuals at the adaptive optimum become genetically less-adaptive, for example due to an environmental change. Furthermore, SGE of unimportant genes might have little or no fitness consequences. Thus, SGE can be advantageous, disadvantageous, or selectively neutral depending on its context. In addition, there might be a genetic basis that regulates magnitude of SGE, which is often referred to as "modifier genes," but little is known about the conditions under which such an SGE-modifier gene evolves. In the present study, we conducted individual-based computer simulations to examine these conditions in a diploid model. In the simulations, we considered a single locus that determines organismal fitness for simplicity, and that SGE on the locus creates fitness variation in a stochastic manner. We also considered another locus that modifies the magnitude of SGE. Our results suggested that SGE was always deleterious in stable environments and increased the fixation probability of deleterious mutations in this model. Even under frequently changing environmental conditions, only very strong natural selection made SGE adaptive. These results suggest that the evolution of SGE-modifier genes requires strict balance among the strength of natural selection, magnitude of SGE, and frequency of environmental changes. However, the degree of dominance affected the condition under which SGE becomes advantageous, indicating a better opportunity for the evolution of SGE in different genetic models.

Keywords: effective population size; environmental change; individual-based simulation; stochastic gene expression; viability selection

References

  1. Science. 2002 Aug 16;297(5584):1183-6 - PubMed
  2. Proc Biol Sci. 2004 Mar 7;271(1538):471-5 - PubMed
  3. Proc Natl Acad Sci U S A. 2004 Jun 15;101(24):9033-8 - PubMed
  4. Nat Rev Genet. 2005 Jun;6(6):451-64 - PubMed
  5. Science. 2005 Sep 23;309(5743):2075-8 - PubMed
  6. Nature. 2005 Sep 29;437(7059):699-706 - PubMed
  7. Science. 2005 Sep 23;309(5743):2010-3 - PubMed
  8. Nature. 2006 Jun 15;441(7095):840-6 - PubMed
  9. Nat Genet. 2007 Aug;39(8):945-9 - PubMed
  10. Mol Syst Biol. 2008;4:170 - PubMed
  11. Nat Genet. 2008 Apr;40(4):471-5 - PubMed
  12. Science. 2008 Apr 4;320(5872):65-8 - PubMed
  13. Genome Res. 2008 Jul;18(7):1084-91 - PubMed
  14. Nat Rev Genet. 2008 Aug;9(8):583-93 - PubMed
  15. Evolution. 2008 Sep;62(9):2155-77 - PubMed
  16. Curr Opin Biotechnol. 2008 Aug;19(4):369-74 - PubMed
  17. Proc Natl Acad Sci U S A. 2008 Nov 11;105(45):17256-61 - PubMed
  18. PLoS Comput Biol. 2008 Nov;4(11):e1000216 - PubMed
  19. Nat Genet. 2009 Apr;41(4):498-503 - PubMed
  20. Proc Natl Acad Sci U S A. 2009 Apr 14;106(15):6410-5 - PubMed
  21. Genetics. 2009 Aug;182(4):1159-64 - PubMed
  22. Mol Syst Biol. 2009;5:299 - PubMed
  23. Plant Biotechnol J. 2011 Feb;9(2):230-49 - PubMed
  24. Development. 2011 Jan;138(2):227-35 - PubMed
  25. Genome Res. 2011 May;21(5):645-57 - PubMed
  26. Proc Natl Acad Sci U S A. 2011 Apr 19;108(16):E67-76 - PubMed
  27. Mol Syst Biol. 2011 May 24;7:493 - PubMed
  28. Nature. 2011 Jul 20;475(7356):308-15 - PubMed
  29. J Exp Bot. 2012 Apr;63(7):2541-56 - PubMed
  30. Plant Cell. 2012 Jun;24(6):2578-95 - PubMed
  31. J Chem Phys. 2012 Jul 21;137(3):035104 - PubMed
  32. Genome Res. 2013 Jul;23(7):1089-96 - PubMed
  33. PLoS One. 2013 May 29;8(5):e64074 - PubMed
  34. Gene. 2015 May 10;562(1):16-21 - PubMed
  35. Elife. 2015 Jun 17;4:null - PubMed
  36. Evolution. 2015 Sep;69(9):2345-58 - PubMed
  37. Evolution. 1994 Oct;48(5):1478-1486 - PubMed
  38. Evolution. 1987 Mar;41(2):303-315 - PubMed

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