DOI: 10.5061/dryad.547d7wm87
title: Amitosis confers benefits of sex in the absence of sex to Tetrahymena

description: 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. 
description: Contents: 
 <strong>README.md</strong> Markdown file containing this
 description.  
 <strong>diploid_amitosis.nb</strong> Mathematica notebook
 containing the analysis of equilibrium mean fitness under amitosis in
 diploids shown in appendix A.   
 <strong>var_fitness.nb </strong>Mathematica notebook
 containing the analysis of the effect of amitosis on the variance in
 fitness summarized in equations 6 and 7. 
 <strong>determin.nb </strong>Mathematica notebook containing
 the analysis of the deterministic benefit of amitosis shown in figure
 2. <strong>muller.ipynb</strong> Jupyter
 notebook that generates figure 3. 
 <strong>muller.csv</strong> 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: <strong>mode</strong> mode of
 reproduction (“Mito” or “Amito”).   
 <strong>ploidy</strong> (2- or 45-ploid).
 <strong>N</strong> population size (10, 100, 1K, or
 10K). <strong>t</strong> time
 (generation). 
 <strong>fit_mean</strong> mean fitness of a population (mean
 of 100 replicate populations).  
 <strong>fit_ci</strong> 95% confidence interval on the mean
 fitness (based on 100 replicate populations). 
 
 <strong>sex.ipynb</strong> Jupyter notebook that
 generates figure 4 showing that the benefit of amitosis is similar to that
 of sex. 
 <strong>sex.csv</strong> 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:
 <strong>mode</strong> mode of
 reproduction (“Mito”, “Amito”, “Sex (tau=1)”, or “Sex
 (tau=100)”).   
 <strong>N</strong> population size (20 or 1K).
 <strong>ben</strong> 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). <strong>t</strong> time
 (generation). 
 <strong>fit_mean</strong> mean fitness of a population (mean
 of 500 replicate populations).  
 <strong>fit_ci</strong> 95% confidence interval on the mean
 fitness (based on 500 replicate populations).
description: 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.