DISSERTATION RESEARCH: Mapping the Molecular Basis of Pleiotropy Across Diverse Environmental Conditions

为了进化以应对环境挑战，生物必须获得新的基因突变。因此，了解个体突变造成的影响的频谱对于预测进化可能采取的路径至关重要。许多生物体面临着环境条件波动的额外挑战，在这种情况下，在一种条件下有益的突变可能在其他条件下就不会有利。特定突变的适合度效应及其对环境条件的依赖可以在实验室微生物种群中系统地测量。以前使用这种方法的大规模研究主要描述了不利的突变。然而，尽管它们很罕见，但有益的突变是进化的主要驱动力。该项目将使用实验室进化和最近开发的测序技术来衡量数千种新的有益突变在几种环境中的影响。这项工作将产生一个大型的公开可用的数据集，详细说明有益突变的影响如何随着环境条件的变化而变化，这将有助于阐明突变影响的模式，有助于在波动的环境条件下对进化进行建模。突变是推动进化的原材料。了解新突变带来的竞争适应能力的好处对于进化的定量建模很重要。过去的工作在表征突变相关的成本和收益方面取得了重大进展。然而，要了解波动条件下的进化，了解突变的适应度效应如何取决于生长条件也是很重要的。这个项目将使用大规模并行的谱系跟踪来表征数千个个体在多种环境条件下自发产生的有益突变的影响。这一广泛的统计数据集将使在波动条件下的演变能够进行数学建模。该项目还将解决进化权衡的遗传基础，使用全基因组测序来识别数百种突变，这些突变在某些情况下有好处，但在另一些情况下会有成本。最终，这个项目将有助于从理论上理解在波动条件下的进化以及进化权衡的生物学基础。

To evolve to meet environmental challenges, organisms must acquire new genetic mutations. Understanding the spectrum of effects caused by individual mutations is therefore central to predicting the paths evolution might take. Many organisms face the additional challenge of fluctuating environmental conditions, where mutations that are beneficial in one condition may not be advantageous in others. The fitness effects of particular mutations, and their dependence on the environmental conditions, can be systematically measured in laboratory microbial populations. Previous large-scale studies using this approach have primarily characterized disadvantageous mutations. Yet despite their rarity, beneficial mutations are the primary drivers of evolution. This project will use laboratory evolution and recently-developed sequencing technology to measure the effects of thousands of novel beneficial mutations in several environments. The work will produce a large, publicly-available dataset detailing how the effects of beneficial mutations change with the environmental condition, which will help illuminate the patterns underlying mutational effects, facilitating the modeling of evolution in fluctuating environmental conditions.Mutations are the raw material that drives evolution. Understanding the competitive fitness benefit conferred by new mutations is important to quantitative modeling of evolution. Past work has made significant progress towards characterizing the costs and benefits associated with mutations. To understand evolution in fluctuating conditions, however, it is also important to know how the fitness effects of mutations depend on the growth condition. This project will use massively-parallel lineage tracking to characterize the effects of thousands of individual, spontaneously arising beneficial mutations across multiple environmental conditions. This broad statistical dataset will enable mathematical modeling of evolution in fluctuating conditions. The project will additionally address the genetic basis of evolutionary tradeoffs, using whole genome sequencing to identify hundreds of mutations with benefits in some conditions but costs in others. Ultimately, this project will contribute to the theoretical understanding of evolution in fluctuating conditions and the biological basis of evolutionary tradeoffs.