Department of Biology, Reed College, Portland, OR 97202, USA.
Philos Trans R Soc Lond B Biol Sci. 2020 Jan 20;375(1790):20190173. doi: 10.1098/rstb.2019.0173. Epub 2019 Dec 2.
Understanding and quantifying the rates of change in the mitochondrial genome is a major component of many areas of biological inquiry, from phylogenetics to human health. A critical parameter in understanding rates of change is estimating the mitochondrial mutation rate (mtDNA MR). Although the first direct estimates of mtDNA MRs were reported almost 20 years ago, the number of estimates has not grown markedly since that time. This is largely owing to the challenges associated with time- and labour-intensive mutation accumulation (MA) experiments. But even MA experiments do not solve a major problem with estimating mtDNA MRs-the challenge of disentangling the role of mutation from other evolutionary forces acting within the cell. Now that it is widely understood that any newly generated mutant allele in the mitochondria will initially be at very low frequency (1/, where is the number of mtDNA molecules in the cell), the importance of understanding the effective population size () of the mtDNA and the size of genetic bottlenecks during gametogenesis and development has come into the spotlight. In addition to these factors regulating the role of genetic drift, advances in our understanding of mitochondrial replication and turnover allow us to more easily envision how natural selection within the cell might favour or purge mutations in multi-copy organellar genomes. Here, we review the unique features of the mitochondrial genome that pose a challenge for accurate MR estimation and discuss ways to overcome those challenges. Estimates of mtDNA MRs remain one of the most widely used parameters in biology, thus accurate quantification and a deeper understanding of how and why they may vary within and between individuals, populations and species is an important goal. This article is part of the theme issue 'Linking the mitochondrial genotype to phenotype: a complex endeavour'.
理解和量化线粒体基因组的变化率是许多生物学研究领域的主要组成部分,从系统发育学到人类健康。理解变化率的一个关键参数是估计线粒体突变率(mtDNA MR)。虽然近 20 年前就已经报道了第一个 mtDNA MR 的直接估计值,但自那时以来,估计值的数量并没有明显增加。这主要是由于与耗时费力的突变积累(MA)实验相关的挑战所致。但是,即使是 MA 实验也没有解决估计 mtDNA MR 的一个主要问题——从细胞内其他进化力量中区分突变作用的挑战。现在人们普遍认为,线粒体中新产生的突变等位基因最初的频率非常低(1/,其中 是细胞中线粒体 DNA 分子的数量),因此理解 mtDNA 的有效种群大小()以及配子发生和发育过程中遗传瓶颈的大小的重要性已经成为焦点。除了这些调节遗传漂变作用的因素外,我们对线粒体复制和周转的理解的进步使我们更容易想象细胞内的自然选择如何有利于或清除多拷贝细胞器基因组中的突变。在这里,我们回顾了线粒体基因组的独特特征,这些特征给准确的 MR 估计带来了挑战,并讨论了克服这些挑战的方法。mtDNA MR 的估计仍然是生物学中使用最广泛的参数之一,因此,准确量化以及深入了解它们在个体、群体和物种内和之间可能如何变化以及为什么会变化,是一个重要目标。本文是“将线粒体基因型与表型联系起来:一项复杂的努力”主题特刊的一部分。