Quantitative approaches for mapping the real-time evolution of the gut microbiota

微生物群落驱动着重要的生物地球化学循环，从海洋到土壤再到人类肠道。高 细胞更新的速度为这些生态系统提供了快速进化的巨大潜力： 我们的肠道微生物群每天都会产生新的突变。我们和其他人最近发现 这些基因变化可以在一定的时间范围内横扫肠道细菌的常驻种群， 几个月到几天这种快速的进化速度被假设为具有重要的实际意义。 从抗生素耐药性突变的传播到粪便微生物组移植的成功， 和其他个性化治疗。尽管这些影响的潜在重要性，我们目前所知甚少 关于在复杂的群落中运作的进化力量，比如肠道微生物群，或者它们是如何 影响（或被影响）周围社区的组成。一个核心挑战是， 缺乏一个群体遗传框架来预测突变如何在具有大量 生态相互作用的菌株。这限制了我们预测微生物群将如何响应 环境扰动，如药物或饮食，或解释功能变化，我们在这些观察 社区随着时间的推移。我的实验室旨在通过结合生物物理学和 群体遗传建模与计算工具的发展，测量在原位进化， 天然和合成肠道群落。我们的长期目标是破译种群遗传“规则”， 管理肠道微生物群的短期演变，并利用这些见解来指导未来的实验和 治疗的努力。在未来五年，我们将通过多管齐下的战略来实现这一目标：首先，我们将 开发新的时间序列方法，用于分析纵向样本中连锁突变的轨迹 人类肠道宏基因组。这些方法将使我们能够解决关键的开放性问题， 自然选择对广泛遗传变异的持续时间，以及它们是否与更广泛的变化相关 在分类学或功能组成上。第二，我们将开发新的方法， 转座子插入文库，以测量自发有益突变的速率和适应性效应， 在无菌小鼠体内，我们将首次量化这种景观如何在物种，饮食， 和社区背景。最后，为了解释这些新数据并提出驱动假设，我们将开发一个 预测生态多样性如何影响短期进化的机制模型框架 竞争共同代谢物的高度多样化社区的动态。这项工作将提供 这是对我们肠道微生物群短期演变的前所未有的洞察，将构成迈向 开发真正的微生物群落动态预测模型。

Microbial communities drive important biogeochemical cycles, from the ocean to the soil to the human gut. High rates of cell turnover provide these ecosystems with an enormous potential for rapid evolutionary change: billions of new mutations are produced within each of our gut microbiota every day. We and others have recently shown that these genetic changes can sweep through resident populations of gut bacteria on timescales ranging from a few months to a few days. This rapid pace of evolution is hypothesized to have important practical consequences, from the spread of antibiotic resistance mutations to the success of fecal microbiome transplants and other personalized therapies. Despite the potential importance of these effects, we currently know very little about the evolutionary forces that operate within complex communities like the gut microbiota, or how they influence (or are influenced by) the composition of the surrounding community. A central challenge is that we lack a population genetic framework for predicting how mutations spread in communities with large numbers of ecologically interacting strains. This limits our ability to predict how microbiota will evolve in response to environmental perturbations such as drugs or diet, or to interpret functional changes that we observe in these communities over time. My lab aims to address this gap in our understanding by combining biophysical and population genetic modeling with the development of computational tools for measuring in situ evolution in both natural and synthetic gut communities. Our long-term goal is to decipher the population genetic “rules” that govern the short-term evolution of the gut microbiota, and to use these insights to guide future experimental and therapeutic efforts. In the next five years, we will pursue this goal through a multi-pronged strategy: First, we will develop new time-series methods for analyzing the trajectories of linked mutations in longitudinally sampled human gut metagenomes. These methods will allow us to address key open questions about the strength and duration of natural selection on sweeping genetic variants, and whether they are correlated with broader shifts in taxonomic or functional composition. Second, we will develop new methods for leveraging widely deployed transposon insertion libraries to measure the rates and fitness effects of spontaneous beneficial mutations in vivo in ex-germ-free mice, and we will quantify for the first time how this landscape varies across species, diets, and community contexts. Finally, to interpret these new data and to craft driving hypotheses, we will develop a mechanistic modeling framework for predicting how ecological diversity influences short-term evolutionary dynamics in highly diverse communities that compete for common metabolites. Together, this work will provide unprecedented insight into the short-term evolution of our gut microbiota, and will constitute a crucial step toward the development of truly predictive models of microbial community dynamics.