Department of Ecology & Evolutionary Biology, University of Arizona.
Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO.
Genome Biol Evol. 2020 Jan 1;12(1):3754-3761. doi: 10.1093/gbe/evz275.
Errors in gene transcription can be costly, and organisms have evolved to prevent their occurrence or mitigate their costs. The simplest interpretation of the drift barrier hypothesis suggests that species with larger population sizes would have lower transcriptional error rates. However, Escherichia coli seems to have a higher transcriptional error rate than species with lower effective population sizes, for example Saccharomyces cerevisiae. This could be explained if selection in E. coli were strong enough to maintain adaptations that mitigate the consequences of transcriptional errors through robustness, on a gene by gene basis, obviating the need for low transcriptional error rates and associated costs of global proofreading. Here, we note that if selection is powerful enough to evolve local robustness, selection should also be powerful enough to locally reduce error rates. We therefore predict that transcriptional error rates will be lower in highly abundant proteins on which selection is strongest. However, we only expect this result when error rates are high enough to significantly impact fitness. As expected, we find such a relationship between expression and transcriptional error rate for non-C→U errors in E. coli (especially G→A), but not in S. cerevisiae. We do not find this pattern for C→U changes in E. coli, presumably because most deamination events occurred during sample preparation, but do for C→U changes in S. cerevisiae, supporting the interpretation that C→U error rates estimated with an improved protocol, and which occur at rates comparable with E. coli non-C→U errors, are biological.
基因转录错误代价高昂,因此生物进化出了防止错误发生或减轻错误代价的机制。漂移障碍假说最简单的解释是,种群规模较大的物种转录错误率会更低。然而,与种群规模较小的物种(例如酿酒酵母)相比,大肠杆菌的转录错误率似乎更高。如果大肠杆菌的选择压力足够强大,足以通过基因层面的稳健性来维持适应,从而减轻转录错误的后果,那么这种情况就可以得到解释。在这种情况下,就不需要低转录错误率和与之相关的全局校对成本。在这里,我们注意到,如果选择能够进化出局部稳健性,那么选择也应该能够降低局部错误率。因此,我们预测在选择压力最强的高度丰富的蛋白质上,转录错误率会更低。然而,我们只期望在错误率高到足以显著影响适应性的情况下才会出现这种结果。正如预期的那样,我们在大肠杆菌中非 C→U 错误(尤其是 G→A)中发现了表达与转录错误率之间的这种关系,但在酿酒酵母中没有发现这种关系。我们在大肠杆菌中没有发现 C→U 变化的这种模式,这可能是因为大多数脱氨酶事件发生在样品制备过程中,但在酿酒酵母中确实存在这种模式,这支持了这样一种解释,即通过改进的方案估计的 C→U 错误率与大肠杆菌中非 C→U 错误率相当,是具有生物学意义的。