Understanding Robustness of a Cooperative Microbial Community during Evolution

多物种微生物群落可以在生产药物和战斗中胜过单一物种 感染.然而，对于有用的社区来说，它必须是“健壮的”，也就是说它必须保留成员 物种和生存的内部和外部扰动。社区健壮性源于交互 社区成员之间的差异，因此可以随着社区成员的发展而迅速变化。迄今为止，我们 对各种形式的鲁棒性以及它们在进化过程中如何变化知之甚少。理论 工作往往基于不切实际的假设，而经验工作主要是观察或相关的。 为了理解社区健壮性以及它们如何随着社区成员的发展而变化， 创建了一个合作酵母社区。它由两种相互依赖的酵母菌株组成， 必需代谢物这两个菌株在繁殖上是分离的，因此可以被视为两个物种。 由于相互依赖，这两种菌株长期共存，因此可以进一步工程化， 在复杂的任务中进行"劳动分工"，例如降解废物的混合物。但这样的 种群仍然会随着人口的减少而灭绝。在这里，我们将研究社区健壮性， 两种常见的外部扰动：极端的人口减少，如在 新宿主的定殖，或逐渐的种群减少，例如在从肠道定期清除期间。我们的目标 来理解这两种形式的鲁棒性，这样我们就可以操纵它们。我们还想了解 健壮性如何随着社区成员的发展和多样化而变化。 我们已经传承了150多代的多个社区。所有社区都变得更加强大， 在严重的人口减少中幸存下来。引人注目的是，对人口逐渐减少的鲁棒性增加了 在一些社区，但在其他社区有所下降。了解社区健壮性以及如何 它们在进化过程中发生变化，我们已经开发了高通量测定方法来测量细胞的表型， 这两种菌株。例如，基于这些测量的数学模型成功地预测了 在祖先社区中，人口严重减少。我们将使用这些数学 模型来预测我们如何有效地改变鲁棒性。我们还将预测哪一个子集的进化 社区成员在改变社区健壮性方面很重要。我们将实验性地测试模型 预测。模型-实验的差异将促使我们发现缺失的重要元素， 社区稳健性，如进化的新的相互作用和罕见的进化基因型与极端 表型我们的工作将提供一个实验和数学方法来了解社区 隐藏着进化的复杂性

Multi-species microbial communities can outperform single species in producing pharmaceuticals and fighting infections. However, for a community to be useful, it must be “robust” in the sense that it must retain member species and survive internal and external perturbations. Community robustness arises from interactions between community members and can thus change rapidly as community members evolve. To date, we understand very little about various forms of robustness and how they change during evolution. Theoretical work is often based on unrealistic assumptions, while empirical work is largely observational or correlational. To understand community robustness and how they might change as community members evolve, we have created a cooperative yeast community. It consists of two mutually-dependent yeast strains exchanging essential metabolites. The two strains are reproductively isolated, and can thus be regarded as two species. Due to mutual dependence, the two strains coexist over a long term and can thus be further engineered to carry out “division of labor” in complex tasks such as degrading a mixture of waste products. However, such a community can still go extinct upon population reduction. Here, we will examine community robustness against two commonly-encountered external perturbations: extreme population reduction such as during the colonization of a new host, or gradual population reduction such as during periodic purge from the gut. We aim to understand these two forms of robustness so that we can manipulate them. We also want to understand how robustness might change as community members evolve and diversify. We have passaged multiple communities for over 150 generations. All communities became more robust in surviving severe population reductions. Strikingly, robustness against gradual population reduction increased in some communities, but it decreased in other communities. To understand community robustness and how they change during evolution, we have developed high-throughput assays to measure phenotypes of cells from the two strains. Mathematical models based on these measurements successfully predicted for example robustness against severe population reduction in the ancestral community. We will use these mathematical models to predict how we might effectively alter robustness. We will also predict which subset of evolved community members are important in altering community robustness. We will experimentally test model predictions. Model-experiment discrepancies will motivate us to uncover missing elements that are important to community robustness, such as evolved new interactions and rare evolved genotypes with extreme phenotypes. Our work will provide an experimental and mathematical approach to understanding communities harboring evolutionary complexity.