论文标题

一个简单的模型,描述了基因组GC含量的演变,在无性再现的生物中随机扰动

A simple model describing evolution of genomic GC content with random perturbations in asexually reproducing organisms

论文作者

Bohlin, Jon

论文摘要

提出了一个模型,将基因组GC内容随时间推移的演变与以突变率为$ \ rightarrow $ GC和GC $ \ rightarrow $。通过使用ItôCilculus,可以表明,如果无性再生生物的突变速率受到随机扰动的影响,随着时间的流逝,这些突变速率随着时间的流逝而变化。例如,一个额外的布朗运动项似乎影响核苷酸的变异性。随机扰动对突变的变异性越大,布朗运动项的影响越强。减少随机扰动的影响,以限制适应性降低和有害突变,可能意味着将资源剥离为基因组修复系统。因此,在许多生物体中看到的稳定突变率可能是减少布朗运动术语影响的进化策略。此外,如果变为基因组GC含量,即可变位点或单个核苷酸多态性(SNP)的GC含量与减少相同的可能性增加,类似于修复酶的基因敲除和去除的选择性压力的敲除,除非基因实验室中的选择性压力,否则该物种可能会持续不足。这些含义仅仅是允许随机扰动会影响AT-和GC突变率的结果,而使用标准的非策略方法也无法获得。最后,在随机环境中介绍了基因组GC含量演化模型与经典的Luria-Delbrück突变模型之间的联系。

A model is presented relating the evolution of genomic GC content over time to AT$\rightarrow$GC and GC$\rightarrow$AT mutation rates. By employing Itô calculus it is shown that if mutation rates in asexually reproducing organisms are subject to random perturbations that can vary over time several implications follow. For instance, an extra Brownian motion term appears influencing nucleotide variability; the greater the variability of the random perturbations on the mutation rates the stronger the impact of the Brownian motion term. Reducing the influence of the random perturbations, to limit fitness decreasing and deleterious mutations, will likely imply divesting resources to genomic repair systems. The stable mutation rates seen in many organisms could thus be an evolved strategy to reduce the influence of the Brownian motion term. Furthermore, if change to genomic GC content, i.e. the GC content of variable sites or single nucleotide polymorphisms (SNPs), is just as likely to increase as to decrease, something that resembles knockout of repair enzymes and removal of selective pressures seen in evolutionary laboratory experiments, the species genome will likely decay unless infinite resources are available. These implications are solely a consequence of allowing random perturbations affect AT- and GC mutation rates and not obtainable using standard non-stochastic methodology. Finally, a connection between the model for genomic GC content evolution and the classical Luria-Delbrück mutation model is presented in a stochastic setting.

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