文章基本信息

标题：Enhanced evolution by stochastically variable modification of epigenetic marks in the early embryo
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作者：Sergio Branciamore ; Andrei S. Rodin ; Arthur D. Riggs 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2014
卷号：111
期号：17
页码：6353-6358
DOI：10.1073/pnas.1402585111
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Evolution by gene duplication is generally accepted as one of the crucial driving forces for the gain of new complexity and functions, but the formation of pseudogenes remains a problem for this mechanism. Here we expand on earlier ideas that epigenetic modifications can drive neo- and subfunctionalization in evolution by gene duplication. We explore the effects of stochastic epigenetic modifications on the evolution (and thus development) of complex organisms in a constant environment. Modeling is done both using a modified genetic drift analytical treatment and computer simulations, which were found to agree. A transposon silencing model is also explored. Some key assumptions made include (i) stochastic, incomplete removal (or addition) of repressive epigenetic marks takes place during a window(s) of opportunity in the zygote and early embryo; (ii) there is no statistical variation of the marks after the window closes; and (iii) the genes affected are sensitive to dosage. Our genetic drift treatment takes into account that after gene duplication the prevailing case upon which selection operates is a duplicate/singlet heterozygote; to the best of our knowledge, this has not been considered in previous treatments. We conclude from our modeling that stochastic epigenetic modifications, with rates consistent with experimental observation, can both increase the rate of gene fixation and decrease pseudogenization, thus dramatically improving the efficacy of evolution by gene duplication. We also find that a transposon silencing model is advantageous for fixation of recessive genes in diploid organisms, especially with large effective population sizes.
关键词：DNA methylation ; mathematical modeling ; computational biology ; molecular evolution ; developmental biology