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

为了进化以应对环境挑战，生物体必须获得新的基因突变。因此，了解由个体突变引起的一系列影响对于预测进化可能采取的路径至关重要。许多生物体还面临环境条件波动带来的额外挑战，在一种条件下有益的突变在另一种条件下可能并不有利。特定突变的适应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突变。然而，尽管它们罕见，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测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、公开的数据集，详细说明有益突变的影响是如何随着环境条件的变化而变化的，这将有助于阐明突变效应背后的模式，促进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。理解新突变所带来的竞争适应度利益对于进化的定量建模是重要的。过去的工作在描述与突变相关的成本和收益方面取得了重大进展。然而，为了理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很重要。该项目将使用大规模平行谱系追踪来描述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。这个广泛的统计数据集将使在波动条件下的进化的数学建模成为可能。该项目还将研究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益，但在其他情况下则有成本。最终，该项目将有助于在波动条件下进化的理论理解和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.