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Data from: Modeling the mito-nuclear compatibility and its role in species identification

Citation

Princepe, Debora; Aguiar, Marcus (2020), Data from: Modeling the mito-nuclear compatibility and its role in species identification, Dryad, Dataset, https://doi.org/10.5061/dryad.2ngf1vhk7

Abstract

Mitochondrial genetic material (mtDNA) is widely used for phylogenetic reconstruction and as a barcode for species identification. The utility of mtDNA in these contexts derives from its particular molecular properties, including its high evolutionary rate, uniparental inheritance, and small size. But mtDNA may also play a fundamental role in speciation -- as suggested by recent observations of coevolution with the nuclear DNA, along with the fact that respiration depends on coordination of genes from both sources. Here we study how mito-nuclear interactions affect the accuracy of species identification by mtDNA, as well as the speciation process itself. We simulate the evolution of a population of individuals who carry a recombining nuclear genome and a mitochondrial genome inherited maternally. We compare a null model fitness landscape that lacks any mito-nuclear interaction against a scenario in which interactions influence fitness. Fitness is assigned to individuals according to their mito-nuclear compatibility, which drives the coevolution of the nuclear and mitochondrial genomes. Depending on the model parameters, the population breaks into distinct species and the model output then allows us to analyze the accuracy of mtDNA barcode for species identification. Remarkably, we find that species identification by mtDNA is equally accurate in the presence or absence of mito-nuclear coupling and that the success of the DNA barcode derives mainly from population geographical isolation during speciation. Nevertheless, selection imposed by mito-nuclear compatibility influences the diversification process and leaves signatures in the genetic content and spatial distribution of the populations, in three ways. First, speciation is delayed and the resulting phylogenetic trees are more balanced. Second, clades in the resulting phylogenetic tree correlate more strongly with the spatial distribution of species and clusters of more similar mtDNA's. Third, there is a substantial increase in the intraspecies mtDNA similarity, decreasing the number of alleles substitutions per locus and promoting the conservation of genetic information. We compare the evolutionary patterns observed in our model to empirical data from copepods (T. californicus). We find good qualitative agreement in the geographic patterns and the topology of the phylogenetic tree, provided the model includes selection based on mito-nuclear interactions. These results highlight the role of mito-nuclear compatibility in the speciation process and its reconstruction from genetic data.

Usage Notes

Supp_SysBio.pdf contains the Supplementary Information to the manuscript.

MitoNuclear.zip is a package with the files to compile the simulation of the population evolution. 

Content of MitoNuclear.zip:

FortranMitoNuclear.f90
FortranTree.f90
MitoNuclear.jpynb
mycolors.py
readme.txt


In order to use the jupyter notebook you need to compile the Fortran files. To do that open a terminal in the folder containing the files and give the following commands:

f2py -c --fcompiler=gnu95 -m FortranMitoNuclear FortranMitoNuclear.f90
and
f2py -c --fcompiler=gnu95 -m FortranTree FortranTree.f90 

These commands should generate the corresponding ".so" files that are called from python.

Make sure you have python installed, together with the packages numpy, matplotlib, jupyter and jupyterlab. 

Open the notebook with the command

jupiter-lab MitoNuclear.jpynb

and run the three modules separately. The first will run the evolution program. The second will plot the population in space, using different colours for different species.
The third will generate the phylogenetic tree and compute the gamma, alpha and Sackin indices. The tips of the tree will have circles coloured according to the species it represents, with the same color code used to plot the population. Colors are listed in the file mycolors.py and can be edited by the user.

Funding

São Paulo Research Foundation (FAPESP), Award: 2016/01343-7

São Paulo Research Foundation (FAPESP), Award: 2018/11187-8

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES), Award: Finance Code 001

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 302049/2015-0

São Paulo Research Foundation (FAPESP), Award: 2019/20271-5

São Paulo Research Foundation (FAPESP), Award: 2016/01343-7