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

预测进化：使用比较实验进化来测试突变、选择和遗传背景对可重复进化的作用

基本信息

批准号：
BB/T012994/1
负责人：
Tiffany Taylor
金额：
$ 60.15万
依托单位：
University of Bath
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FT012994%2F1
关键词：
Predicting evolution using comparative experimental

项目摘要

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.

为了预测进化论，我们需要知道什么？很长一段时间以来，我们一直能够预测一个种群中已知突变的命运。然而，一项更困难的任务是预测哪些突变可能出现，以及这些突变在先前存在的遗传背景下的后果。我们知道有某些偏见会使某些突变比其他突变更有可能发生，我们也知道突变对个人适应性的影响可能会因个人已经携带的突变而有所不同。但是，我们还没有把这些信息结合在一起，以便能够有效地预测不断演变的种群中可能的适应性突变。预测种群的适应性进化的能力具有许多重要的意义。它将提高我们预测抗生素、除草剂和杀虫剂抗药性的能力；提供机会来优化癌症和传染病的治疗策略；以及在快速变化的气候中，允许进行战略操作，以限制对生态系统和处于危险中的种群的有害影响。为了解决这个问题，我们将比较同一物种的两种细菌菌株的进化轨迹，它们对相同的选择性挑战表现出不同的适应路线-一个可重复，另一个变量。我之前的工作使用了实验室中微生物的实时进化来表明，不活动的细菌可以在96小时内重新进化出运动性。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细菌菌株上重复了这项实验：它们也能够通过相同基因的突变，或在相同的基因网络中，在96小时内恢复活力，但绝不是在相同的位置。这些细菌的亲缘关系足够密切，以至于它们共享大部分基因，这些基因具有相同的功能。然而，它们的基因组也存在一些差异。通过在相同的选择条件下比较这两种菌株，我们能够在实验中测试是什么导致了赋予运动力的进化突变的差异，以及这些差异在驱动可预测的进化轨迹方面的后果。我们将测试是否存在可能偏向哪些突变正在出现的因素。为了做到这一点，我们将进化这两个细菌菌株，它们已经被基因工程改造为非运动性的，在正选择和没有选择的情况下进行运动性选择。我们将寻找在这两个细菌株之间拯救活力的突变频率的差异。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，我们将相互产生突变，这些突变赋予两个菌株中的运动性，并测量这些突变在不同菌株之间的适合性效果。这将告诉我们基因组的差异如何转化为相同基因中相同突变的不同适应度效应。最后，我们将在每个菌株之间的相互运动线上保持对运动性的选择，并选择运动更快的细菌。我们将研究导致细菌移动更快的突变是否依赖于之前的突变，以及某些突变在一种菌株中是否比另一种更常见。这将告诉我们，在不同的细菌中，某些突变路线是如何获得不同的适应度影响的。通过深入理解在一个简单和易处理的系统中决定可重复进化的规则，我们可以为更通用的规则集奠定基础。这里发现的原理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如何有助于可获得的适应性突变。长期目标是提高对更广泛的适应性进化的预测能力。