Evolution of base-substitution gradients in primate mitochondrial genomes.
ABSTRACT Inferences of phylogenies and dates of divergence rely on accurate modeling of evolutionary processes; they may be confounded by variation in substitution rates among sites and changes in evolutionary processes over time. In vertebrate mitochondrial genomes, substitution rates are affected by a gradient along the genome of the time spent being single-stranded during replication, and different types of substitutions respond differently to this gradient. The gradient is controlled by biological factors including the rate of replication and functionality of repair mechanisms; little is known, however, about the consistency of the gradient over evolutionary time, or about how evolution of this gradient might affect phylogenetic analysis. Here, we evaluate the evolution of response to this gradient in complete primate mitochondrial genomes, focusing particularly on A-->G substitutions, which increase linearly with the gradient. We developed a methodology to evaluate the posterior probability densities of the response parameter space, and used likelihood ratio tests and mixture models with different numbers of classes to determine whether groups of genomes have evolved in a similar fashion. Substitution gradients usually evolve slowly in primates, but there have been at least two large evolutionary jumps: on the lineage leading to the great apes, and a convergent change on the lineage leading to baboons (Papio). There have also been possible convergences at deeper taxonomic levels, and different types of substitutions appear to evolve independently. The placements of the tarsier and the tree shrew within and in relation to primates may be incorrect because of convergence in these factors.
[Show abstract] [Hide abstract]
ABSTRACT: Based on the k-mer model for genetic sequence, a k-mer sparse matrix representation is proposed to denote the types and sites of k-mers appearing in a genetic sequence, and there exists a one-to-one relationship between a genetic sequence and its associated k-mer sparse matrix. With the singular value decomposition of the k-mer sparse matrix, the k-mer singular value vector is constructed and utilized to numerically quantify the characteristics of a genetic sequence. We investigate and evaluate the optimum value k⁎ chosen for our k-mer sparse matrix model for genetic sequence. To show the usefulness of our k-mer sparse matrix model method, it is applied to the comparison of genetic sequences, and the results obtained fully demonstrate that our proposed method is very powerful in analyzing and determining the relationships of genetic sequences.Journal of Theoretical Biology 08/2014; 363. DOI:10.1016/j.jtbi.2014.08.028 · 2.30 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Mitochondrial genomes are known to have a strong strand-specific compositional bias that is more pronounced at fourfold redundant sites of mtDNA protein-coding genes. This observation suggests that strand asymmetries, to a large extent, are caused by mutational asymmetric mechanisms. In vertebrate mitogenomes, replication and not transcription seems to play a major role in shaping compositional bias. Hence, one can better understand how mtDNA is replicated - a debated issue - through a detailed picture of mitochondrial genome evolution. Here, we analyzed the compositional bias (AT and GC skews) in protein-coding genes of almost 2,500 complete vertebrate mitogenomes. We were able to identify three fish mitogenomes with inverted AT/GC skew coupled with an inversion of the Control Region. These findings suggest that the vertebrate mitochondrial replication mechanism is asymmetric and may invert its polarity, with the leading-strand becoming the lagging-strand and vice-versa, without compromising mtDNA maintenance and expression. The inversion of the strand-specific compositional bias through the inversion of the Control Region is in agreement with the strand-displacement model but it is also compatible with the RITOLS model of mtDNA replication.PLoS ONE 09/2014; 9(9):e106654. DOI:10.1371/journal.pone.0106654 · 3.53 Impact Factor
Molecular Phylogenetics and Evolution 01/2013; 66(1):69-79. · 4.02 Impact Factor