Fitness landscape of a dynamic RNA structure.
Fitness landscape of a dynamic RNA structure.
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一个动态RNA结构的适合度景观。
DOI：
10.1371/journal.pgen.1009353
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发表时间：
2021-03
期刊：
影响因子：
4.5
通讯作者：
Warnecke T
中科院分区：
文献类型：
--
作者：
Soo VWC;Swadling JB;Faure AJ;Warnecke T
RNA结构是动态的。因此，突变效应很难根据单个静态原生结构来合理化。我们推断，将分子功能与适应性结合起来的深度突变扫描实验应该同时捕获多个构象状态的突变效应。在这里，我们提供了一个证明的原则，这确实是这样的情况下，使用自我剪接组I的内含子从四膜虫嗜热菌作为一个模型系统。我们对内含子的两个4-bp片段进行了综合诱变。这些片段首先聚集在一起，在5'剪接位点形成P1延伸（P1 ex）螺旋。在5'剪接位点处切割后，螺旋的两半解离以允许在3'剪接位点处形成交替螺旋（P10）。使用体内报告系统，在大肠杆菌中将剪接活性与适应性偶联。大肠杆菌，我们证明了健身是共同驱动的P1 ex和P10形成的限制。我们进一步表明，模式的上位性可以用来推断存在的分子内多效性。使用机器学习的方法，允许量化的突变效应的基因型特异性的方式，我们证明，健身景观可以去卷积牵连P1 ex或P10作为有效的遗传背景，其中分子健身是妥协或增强。我们的研究结果强调了深度突变扫描作为研究替代构象状态的工具，能够为RNA作为动态集合的结构，进化和可进化性提供关键的见解。我们的研究结果还表明，在未来，深度突变扫描方法可能有助于从单一的健身景观逆向工程多个替代或连续的构象。现在，几乎可以随意地将突变引入编码RNA和蛋白质的基因中。然而，为什么一种突变会损害分子的功能，而另一种则不会，这一点仍然不清楚。这在一定程度上是因为我们理解差异突变效应的分子基础的主要标志-晶体结构-只提供了非常部分的指导。特别是RNA是高度动态的，并且在RNA在正常功能期间呈现的多种构象期间可能出现缺陷。一个单晶结构可能只是一个大系综中所有重要构象的快照。在这里，我们表明，深度突变扫描技术，以产生一个大型库的突变版本的原始分子，可以同时捕捉突变的影响，发挥其影响的几个构象之一的分子假设在其生命周期。因此，原则上，深度突变扫描可以用于研究瞬时或难以观察的构象，并更好地了解突变为什么以及何时有害。
RNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to fitness, should capture mutational effects across multiple conformational states simultaneously. Here, we provide a proof-of-principle that this is indeed the case, using the self-splicing group I intron from Tetrahymena thermophila as a model system. We comprehensively mutagenized two 4-bp segments of the intron. These segments first come together to form the P1 extension (P1ex) helix at the 5’ splice site. Following cleavage at the 5’ splice site, the two halves of the helix dissociate to allow formation of an alternative helix (P10) at the 3’ splice site. Using an in vivo reporter system that couples splicing activity to fitness in E. coli, we demonstrate that fitness is driven jointly by constraints on P1ex and P10 formation. We further show that patterns of epistasis can be used to infer the presence of intramolecular pleiotropy. Using a machine learning approach that allows quantification of mutational effects in a genotype-specific manner, we demonstrate that the fitness landscape can be deconvoluted to implicate P1ex or P10 as the effective genetic background in which molecular fitness is compromised or enhanced. Our results highlight deep mutational scanning as a tool to study alternative conformational states, with the capacity to provide critical insights into the structure, evolution and evolvability of RNAs as dynamic ensembles. Our findings also suggest that, in the future, deep mutational scanning approaches might help reverse-engineer multiple alternative or successive conformations from a single fitness landscape. Mutations can now be introduced into genes that code for RNAs and proteins almost at will. Yet why one mutation compromises the function of the molecule while another does not often remains unclear. This is, in part, because our main signposts for understanding the molecular basis of differential mutational effects—crystal structures–provide only very partial guidance. RNAs in particular are highly dynamic and defects can arise during multiple conformations that the RNA assumes during normal function. A single crystal structure might represent but a snapshot of all the important conformations in a large ensemble. Here we show that deep mutational scanning–a technique to generate a large library of mutated versions of the original molecule–can simultaneously capture the impact of mutations that exert their effect in one of several conformations the molecule assumes during its life cycle. Deep mutational scanning can therefore be used, in principle, to study conformations that are transient or hard to observe and to better understand why and when mutations are harmful.
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影响因子：
5.5
作者：
Goetz, Andreas W.;Williamson, Mark J.;Xu, Dong;Poole, Duncan;Le Grand, Scott;Walker, Ross C.
通讯作者：
Walker, Ross C.
影响因子：
16.6
作者：
Kobori, Shungo;Yokobayashi, Yohei
通讯作者：
Yokobayashi, Yohei
DOI：
10.1038/s41580-019-0136-0
发表时间：
2019-08
期刊：
Nature reviews. Molecular cell biology
影响因子：
--
作者：
Ganser LR;Kelly ML;Herschlag D;Al-Hashimi HM
通讯作者：
Al-Hashimi HM
影响因子：
6.3
作者：
Le Grand, Scott;Goetz, Andreas W.;Walker, Ross C.
通讯作者：
Walker, Ross C.
影响因子：
64.8
作者：
Hayden, Eric J.;Ferrada, Evandro;Wagner, Andreas
通讯作者：
Wagner, Andreas