Fitness effects of altering gene expression noise in Saccharomyces cerevisiae.
Fitness effects of altering gene expression noise in Saccharomyces cerevisiae.
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DOI：
10.7554/elife.37272
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发表时间：
2018-08-20
期刊：
影响因子：
7.7
通讯作者：
Wittkopp PJ
中科院分区：
文献类型：
--
作者：
Duveau F;Hodgins-Davis A;Metzger BP;Yang B;Tryban S;Walker EA;Lybrook T;Wittkopp PJ
基因表达噪声是生物系统的一种可进化特性，它描述了在相同环境中遗传相同的细胞之间的表达差异。先前的工作表明，表达噪音是可遗传的，可以通过选择来塑造，但表达噪音的变化对生物体适应度的影响已被证明难以衡量。在这里，我们量化的健身效果改变表达噪音的TDH 3基因在酿酒酵母。我们表明，表达噪声的增加可能是有害的或有益的，这取决于基因型的平均表达水平和最大化适应性的表达水平之间的差异。我们还表明，一个简单的模型与单细胞表达水平的人口增长产生的模式与我们的经验数据一致。我们使用这个模型来探索广泛的平均表达水平和表达噪声，为表达噪声变化的适应性效应提供了额外的见解。通过分裂繁殖的单细胞生物，如酵母，可以创造出基因完全相同的整个细胞群。然而，即使这些细胞都经历过相同的环境，它们之间也会存在一些差异。这些差异被称为“噪音”。根据定义，噪声不是由DNA序列的差异引起的，而是某些DNA序列比其他DNA序列更有噪声（即它们在细胞之间引起更多的差异）。由于噪音的数量可以在遗传控制下，噪音可以由于自然选择而进化。科学家经常在基因表达水平上研究噪音-换句话说，每个细胞内的每个基因产生多少RNA或蛋白质分子。先前的工作表明，这种类型的噪音可以影响群体中单个细胞分裂的频率，这是该群体适应性的一个组成部分。然而，直接测量这些影响已被证明具有挑战性。过去，不同的研究得出了相反的结论，即基因表达噪音的变化是否会增加或减少适应性。缺乏明确结果的一个主要原因是，大多数改变基因表达噪音的突变也改变了该基因的平均表达水平。为了找到产生相同平均蛋白质量但不同表达噪音水平的DNA序列，Duveau等人比较了调节面包酵母中基因表达的DNA序列中数百个突变的影响。针对43个DNA序列的实验表明，增加的表达噪音可以通过影响每个细胞分裂所需的时间来加速或减缓种群的增长。更具体地说，增加表达噪音的影响取决于群体中细胞之间产生的蛋白质的平均量。如果平均表达水平接近细胞尽可能快地分裂的最佳量，则增加表达噪声会降低整个群体的生长。然而，如果平均蛋白质水平导致细胞分裂速度低于其最大速度，那么增加表达噪音会导致整个群体的更快增长。Duveau等人对他们的结果解释如下：在已经产生最佳蛋白质量的群体中，更多的表达噪音会降低适应性，因为它增加了该群体中产生次优蛋白质量的比例。然而，当平均表达水平不是最佳时，更多的表达噪音将意味着更多的细胞产生更接近最佳的蛋白质量，从而具有更高的适应性。这些发现提供了理解遗传变异如何影响表达噪音进化所需的概念工具。它们还可以帮助理解表达噪音如何影响依赖于细胞分裂的生物过程，例如癌症等疾病。
Gene expression noise is an evolvable property of biological systems that describes differences in expression among genetically identical cells in the same environment. Prior work has shown that expression noise is heritable and can be shaped by selection, but the impact of variation in expression noise on organismal fitness has proven difficult to measure. Here, we quantify the fitness effects of altering expression noise for the TDH3 gene in Saccharomyces cerevisiae. We show that increases in expression noise can be deleterious or beneficial depending on the difference between the average expression level of a genotype and the expression level maximizing fitness. We also show that a simple model relating single-cell expression levels to population growth produces patterns consistent with our empirical data. We use this model to explore a broad range of average expression levels and expression noise, providing additional insight into the fitness effects of variation in expression noise. Single-celled organisms that reproduce by dividing, like yeast, can create whole populations of genetically identical cells. However, some differences will exist among such cells, even when they have all experienced the same environment. These differences are known as “noise”. By definition, noise is not caused by differences in DNA sequence, but some DNA sequences are noisier than others (i.e. they cause more differences among cells). Because the amount of noise can be under genetic control, noise could evolve due to natural selection. Scientists often study noise at the level of gene expression – in other words, how many RNA or protein molecules are produced from each gene within each cell. Prior work has suggested that this type of noise can affect how often individual cells divide in a population, which is a component of that population’s fitness. Yet directly measuring these effects has proven challenging. Different studies have in the past reached opposite conclusions about whether a change in gene expression noise would increase or decrease fitness. One major reason for the lack of clear results is that most mutations that alter gene expression noise also alter the average level of expression of that gene. To find DNA sequences that produced the same average amount of protein but different levels of expression noise, Duveau et al. compared the effects of hundreds of mutations in the DNA sequence regulating the expression of a gene in baker’s yeast. Experiments focused on 43 DNA sequences then showed that increased expression noise could either speed up or slow down the growth of the population by affecting how long it took each cell to divide. More specifically, the effects of increasing expression noise depended on the average amount of protein produced among the cells in the population. If the average expression level was close to the optimum amount at which cells divided as fast as possible, increasing expression noise reduced the growth of the whole population. If, however, the average protein level caused cells to divide slower than their maximum rate, increasing expression noise resulted in faster growth of the population as a whole. Duveau et al. explain their results as follows: more expression noise in a population that is already making the optimal amount of protein can reduce fitness because it increases the fraction of that population making a suboptimal amount of the protein. However, when the average expression level is not optimal, more expression noise would mean more cells producing an amount of protein that is closer to the optimum and thus having higher fitness. These findings provide conceptual tools needed to understand how genetic variation affecting expression noise evolves. They could also help understand how expression noise might contribute to biological processes that depend upon cell division, such as diseases like cancer.