Predicting evolution: using comparative experimental evolution to test the role of mutation, selection and genetic background on repeatable evolution

为了预测进化，我们需要知道什么？很长一段时间以来，我们一直能够预测种群中已知突变的命运。然而，更困难的任务是预测哪些突变可能出现，以及这些突变在现有遗传背景下的后果。我们知道，存在某些偏差，使某些突变比其他突变更容易发生，我们也知道，突变对个体适应性的影响可能因该个体已经携带的突变而异。但是，我们还没有把这些信息整合在一起，以便有效地预测进化群体中可能出现的适应性突变。预测种群适应性进化的能力具有许多重要意义。它将提高我们预测抗生素、除草剂和农药耐药性的能力；提供机会，优化癌症和传染病的治疗战略；在气候迅速变化的情况下，允许采取战略行动，限制对生态系统和处于危险中的人口的有害影响。为了解决这个问题，我们将比较同一物种的两种细菌的进化轨迹，这两种细菌对相同的选择挑战表现出不同的适应途径——一种是可重复的，另一种是可变的。我之前的工作是在实验室里利用微生物的实时进化来证明一种不运动的细菌可以在96小时内重新进化出运动能力。有趣的是，我们在90%的病例中发现了相同的突变。我们在同一物种的不同菌株中重复了这个实验：它们也能够在96小时内通过相同基因的突变或相同基因网络的突变来恢复运动能力，但从来没有在相同的位置。这些细菌关系密切，它们共享大部分基因，这些基因执行相同的功能。然而，他们的基因组中也存在一些差异。通过在相同的选择条件下比较这两种菌株，我们能够通过实验测试什么可能导致赋予运动性的进化突变的差异，以及这些差异在驱动可预测的进化轨迹方面的后果。我们将测试是否有因素可能会影响哪些突变正在出现。为了做到这一点，我们将进化两种细菌菌株，它们在积极选择和缺乏运动选择的情况下，通过基因工程使其不具有运动能力。我们将寻找拯救两种细菌菌株运动的突变频率的差异。这将告诉我们，基因组结构或组成的差异是否可能导致可获得的突变。接下来，我们将相互产生赋予任一菌株运动性的突变，并测量这些突变在菌株之间的适应度效应。这将告诉我们基因组的差异是如何转化为相同基因中相同突变的不同适应度效应的。最后，我们将在每个菌株的相互运动线中保持运动性选择，并选择运动速度更快的细菌。我们将观察赋予细菌更快移动的突变是否依赖于它之前的突变，以及某些突变是否在一种菌株中比在另一种菌株中更常见。这将告诉我们，在不同细菌的适应度效应不同的情况下，某些突变途径是如何容易获得的。通过深入了解决定简单和可处理系统中可重复进化的规则，我们可以为更一般化的规则集建立基础。这里发现的原则将有助于我们更好地理解潜在的突变偏见和更广泛的遗传背景是如何促成可获得的适应性突变的。长期目标是提高预测适应性进化的能力。

What do we need to know in order to predict evolution? For a long time we have been able to predict the fate of a known mutation within a population. However, a more difficult task is predicting which mutations are likely to emerge, and the consequences of those mutations within the context of the pre-existing genetic background. We know that there are certain biases that make some mutations more likely to occur than others, and we know that the effect of mutations on an individual's fitness can vary depending on the mutations already carried by that individual. But, we have yet to bring these pieces of information together to enable effective forecasting of likely adaptive mutations in an evolving population.The ability to forecast adaptive evolution of populations has many important implications. It will improve our ability to predict antibiotic, herbicide and pesticide resistance; grant opportunities to optimise treatment strategies for cancers and infectious diseases; and in a rapidly changing climate, allow strategic manoeuvres to limit detrimental effects to ecosystems and at risk populations.To address this problem, we will compare the evolutionary trajectories of two strains of bacteria of the same species that show different adaptive routes to the same selective challenge - one repeatable the other variable. My previous work has used real-time evolution of microbes in the laboratory to show that a non-motile bacteria can re-evolve motility within 96 hours. Interestingly, we found the same mutation in 90% of cases. We repeated this experiment in a different strain of bacteria of the same species: they were also able to rescue motility within 96 hours via mutations in the same genes, or within the same network of genes, but never at the same site. These bacteria are closely related enough such that they share most of their genes and these genes carry out the same functions. However, they also carry a number of differences across their genomes. By comparing these two strains under the same selective conditions, we are able to experimentally test what might be causing differences in the evolved mutations conferring motility, and the consequences of these differences in driving predictable evolutionary trajectories.We will test whether there are factors that might be biasing which mutations are emerging. To do this we will evolve both bacterial strains, which have been genetically engineered to be non-motile, under positive selection and in the absence of selection for motility. We will look for differences in the frequency of mutations that rescue motility across the two bacterial strains. This will inform us as to whether differences in the structure or composition of the genome might be contributing to accessible mutations. Next, we will reciprocally generate mutations that confer motility in either strain and measure the fitness effect of these mutations across strains. This will tell us how differences in the genome translate into different fitness effects of the same mutations in the same genes. Finally, we will maintain selection for motility in the reciprocal motile lines across each strain, and select for faster motile bacteria. We will look to see whether mutations that confer a faster moving bacteria are dependent on the mutations that precede it, and whether certain mutations are more common in one strain compared to the other. This will tell us how accessible certain mutational routes are given differences in fitness effects across different bacteria.By gaining an in depth understanding of the rules that determine repeatable evolution in a simple and tractable system, we can build the foundation for a more generalised ruleset. The principals discovered here will help improve our understanding of how underlying mutational biases and the wider genetic background contribute to accessible adaptive mutations. With a long term goal of improved ability to forecast adaptive evolution more generally.