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Amitosis confers benefits of sex in the absence of sex to Tetrahymena

Citation

Zhang, Hao; West, Joe; Zufall, Rebecca; Azevedo, Ricardo (2021), Amitosis confers benefits of sex in the absence of sex to Tetrahymena, Dryad, Dataset, https://doi.org/10.5061/dryad.547d7wm87

Abstract

Sex appears to be the most successful reproductive strategy in eukaryotes despite its many costs.  While a complete explanation for sex's success remains elusive, several evolutionary benefits of sex have been identified. It is predicted that, by forgoing these benefits, asexual lineages are evolutionary dead-ends. Consistent with this prediction, many asexual lineages show signs of accelerated accumulation of deleterious mutations compared to their sexual relatives.  Despite these low expectations, some asexual eukaryotic lineages appear to be successful, including the ciliate Tetrahymena.  Here, we show that the mechanism of somatic nuclear division in Tetrahymena, known as amitosis, provides benefits similar to sex, allowing for the long-term success of asexual lineages.  We found that, when compared to mitosis, amitosis with chromosome copy number control reduces mutation load deterministically, slows the accumulation of deleterious mutations under genetic drift, and accelerates adaptation.  These benefits arise because, like sex, amitosis can generate substantial genetic variation in fitness among (asexual) progeny.  Our results indicate that the ability of Tetrahymena to persist in the absence of sex may depend on non-sexual genetic mechanisms conferring benefits typically provided by sex, as has been found in other asexual lineages. 

Methods

Contents:

  • README.md Markdown file containing this description. 
  • diploid_amitosis.nb Mathematica notebook containing the analysis of equilibrium mean fitness under amitosis in diploids shown in appendix A.  
  • var_fitness.nb Mathematica notebook containing the analysis of the effect of amitosis on the variance in fitness summarized in equations 6 and 7.
  • determin.nb Mathematica notebook containing the analysis of the deterministic benefit of amitosis shown in figure 2.
  • muller.ipynb Jupyter notebook that generates figure 3.
  • muller.csv Data from stochastic simulations used to generate figure 3. The data consist of changes in fitness in stochastic evolutionary simulations under different modes of reproduction, ploidies, and population sizes. We assumed \(L=100\) fitness loci, a genomic deleterious mutation rate of \(U_d=0.1\) per generation, that mutations have a deleterious effect of \(s_d=–0.1\) in a homozygous state, and that, initially, all individuals are unmutated. The file contains 6 columns:
    • mode mode of reproduction (“Mito” or “Amito”).  
    • ploidy (2- or 45-ploid).
    • N population size (10, 100, 1K, or 10K).
    • t time (generation).
    • fit_mean mean fitness of a population (mean of 100 replicate populations). 
    • fit_ci 95% confidence interval on the mean fitness (based on 100 replicate populations).
  • sex.ipynb Jupyter notebook that generates figure 4 showing that the benefit of amitosis is similar to that of sex.

  • sex.csv data from stochastic simulations used to generate figure 4. The data consist of changes in fitness in stochastic evolutionary simulations under different modes of reproduction and mutational parameters. We assumed \(L=100\) fitness loci, a genomic deleterious mutation rate of \(U_d=0.1\) per generation, that mutations have a deleterious effect of \(s_d=–0.1\) in a homozygous state, and that, initially, all individuals are unmutated. The file contains 5 columns:
    • mode mode of reproduction (“Mito”, “Amito”, “Sex (tau=1)”, or “Sex (tau=100)”).  
    • N population size (20 or 1K).
    • ben whether beneficial mutations occur (if True, the beneficial mutation rate was \(U_b=0.001\) and the beneficial effect of a mutation was \(s_b=0.1\) in a homozygous state).
    • t time (generation).
    • fit_mean mean fitness of a population (mean of 500 replicate populations). 
    • fit_ci 95% confidence interval on the mean fitness (based on 500 replicate populations).

Usage Notes

  • The Mathematica notebooks were last tested with Mathematica 12.2.0.0.
  • The Jupyter notebooks were last tested with python 3.7.10, pandas 1.2.4, matplotlib 3.4.1,  and seaborn 0.11.1.

Funding

National Institutes of Health, Award: R01GM101352

National Science Foundation, Award: DEB-1911449

National Science Foundation, Award: DEB-1354952

National Science Foundation, Award: DEB-2014566