生命之树转录错误率的狭窄范围。

Weiyi Li, Stephan Baehr, Michelle Marasco, Lauren Reyes, Danielle Brister, Craig S Pikaard, Jean-Francois Gout, Marc Vermulst, Michael Lynch
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引用次数: 0

摘要

基因组编码信息的表达并非没有错误。转录错误率远高于dna水平的突变率,尽管它们是短暂的,但这种错误的稳态负荷必然会给细胞性能带来一些负担。然而,转录错误率在多大程度上受到自然选择的限制以及在谱系之间的分化仍有待进一步研究。在这里,我们使用改进的滚动圈测序方法对生命之树的转录错误率进行了全基因组分析,揭示了错误率的范围在不同物种之间非常狭窄。转录错误往往是随机分布的,很少有证据支持与基因表达水平相关的错误率的局部控制。如果翻译,大多数转录错误会导致误义错误,并且与一小部分无意义转录错误一样,这些错误相对于随机期望而言代表性不足,这表明存在清除某些此类错误的机制。为了定量地了解自然选择和随机遗传漂变如何影响物种间的转录错误率,我们提出了一个基于细胞生物学和群体遗传学的模型,结合了细胞体积、蛋白质组大小、个体错误的平均暴露程度和有效群体大小等信息。然而,尽管该模型为理解这一高度保守特征的进化提供了一个框架,但就目前的结构而言,它只能解释数据中20%的变化,这表明需要在这一领域开展进一步的理论工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Narrow Range of Transcript-error Rates Across the Tree of Life.

The expression of genomically-encoded information is not error-free. Transcript-error rates are dramatically higher than DNA-level mutation rates, and despite their transient nature, the steady-state load of such errors must impose some burden on cellular performance. However, a broad perspective on the degree to which transcript-error rates are constrained by natural selection and diverge among lineages remains to be developed. Here, we present a genome-wide analysis of transcript-error rates across the Tree of Life using a modified rolling-circle sequencing method, revealing that the range in error rates is remarkably narrow across diverse species. Transcript errors tend to be randomly distributed, with little evidence supporting local control of error rates associated with gene-expression levels. A majority of transcript errors result in missense errors if translated, and as with a fraction of nonsense transcript errors, these are underrepresented relative to random expectations, suggesting the existence of mechanisms for purging some such errors. To quantitatively understand how natural selection and random genetic drift might shape transcript-error rates across species, we present a model based on cell biology and population genetics, incorporating information on cell volume, proteome size, average degree of exposure of individual errors, and effective population size. However, while this model provides a framework for understanding the evolution of this highly conserved trait, as currently structured it explains only 20% of the variation in the data, suggesting a need for further theoretical work in this area.

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