Li Weiyi, Baehr Stephan, Marasco Michelle, Reyes Lauren, Brister Danielle, Pikaard Craig S, Gout Jean-Francois, Vermulst Marc, Lynch Michael
Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287, USA.
Sci Adv. 2025 Jul 11;11(28):eadv9898. doi: 10.1126/sciadv.adv9898.
Although transcript-error rates are markedly higher than DNA-level mutation rates, a broad perspective on the degree to which they diverge across lineages remains to be developed. Using modified rolling-circle sequencing, we found a narrow range of transcript-error rates across the Tree of Life, with little evidence supporting local control of error rates associated with gene expression levels. Most errors result in missense changes if translated, and, as with a fraction of nonsense errors, these are underrepresented relative to random expectations, suggesting the existence of mechanisms for purging some such errors. To understand how natural selection and random genetic drift might shape transcript-error rates, we present a model based on cell biology and population genetics. However, while this framework helps understand 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.
尽管转录错误率明显高于DNA水平的突变率，但对于它们在不同谱系间的差异程度，仍有待形成一个全面的认识。通过使用改良的滚环测序技术，我们发现在整个生命之树中，转录错误率的范围很窄，几乎没有证据支持错误率与基因表达水平存在局部调控关系。如果进行翻译，大多数错误会导致错义变化，并且与一部分无义错误一样，相对于随机预期，这些错误的出现频率较低，这表明存在清除某些此类错误的机制。为了理解自然选择和随机遗传漂变如何影响转录错误率，我们提出了一个基于细胞生物学和群体遗传学的模型。然而，尽管这个框架有助于理解这一高度保守性状的进化，但按照目前的结构，它只能解释数据中20%的变异，这表明该领域需要进一步开展理论研究。
