Division of Nutritional Sciences, Department of Molecular Biology and Genetics, and Tri-Institutional Training Program in Computational Biology and Medicine, Cornell University, Ithaca, NY 14853, USA.
Proc Natl Acad Sci U S A. 2012 Jun 26;109(26):10420-5. doi: 10.1073/pnas.1121507109. Epub 2012 Jun 11.
Epistasis refers to the phenomenon in which phenotypic consequences caused by mutation of one gene depend on one or more mutations at another gene. Epistasis is critical for understanding many genetic and evolutionary processes, including pathway organization, evolution of sexual reproduction, mutational load, ploidy, genomic complexity, speciation, and the origin of life. Nevertheless, current understandings for the genome-wide distribution of epistasis are mostly inferred from interactions among one mutant type per gene, whereas how epistatic interaction partners change dynamically for different mutant alleles of the same gene is largely unknown. Here we address this issue by combining predictions from flux balance analysis and data from a recently published high-throughput experiment. Our results show that different alleles can epistatically interact with very different gene sets. Furthermore, between two random mutant alleles of the same gene, the chance for the allele with more severe mutational consequence to develop a higher percentage of negative epistasis than the other allele is 5070% in eukaryotic organisms, but only 2030% in bacteria and archaea. We developed a population genetics model that predicts that the observed distribution for the sign of epistasis can speed up the process of purging deleterious mutations in eukaryotic organisms. Our results indicate that epistasis among genes can be dynamically rewired at the genome level, and call on future efforts to revisit theories that can integrate epistatic dynamics among genes in biological systems.
上位性是指一个基因突变引起的表型后果取决于另一个或多个基因突变的现象。上位性对于理解许多遗传和进化过程至关重要，包括途径组织、有性生殖的进化、突变负荷、倍性、基因组复杂性、物种形成和生命的起源。然而，目前对于全基因组上位性分布的理解大多是从每个基因的一种突变类型之间的相互作用推断出来的，而对于同一基因的不同突变等位基因的上位性相互作用伙伴如何动态变化，目前还知之甚少。在这里，我们通过结合通量平衡分析的预测和最近发表的高通量实验的数据来解决这个问题。我们的结果表明，不同的等位基因可以与非常不同的基因集发生上位性相互作用。此外，在同一个基因的两个随机突变等位基因之间，突变后果更严重的等位基因比另一个等位基因更容易发展出更高比例的负上位性的机会在真核生物中是 50%70%，但在细菌和古菌中只有 20%30%。我们开发了一个群体遗传学模型，该模型预测观察到的上位性符号的分布可以加速真核生物中有害突变的清除过程。我们的结果表明，基因之间的上位性可以在基因组水平上动态重布线，并呼吁未来的研究努力重新审视可以整合生物系统中基因之间上位性动态的理论。
