Understanding Robustness of a Cooperative Microbial Community during Evolution

多物种微生物群落在药物生产和战斗中的表现可以超过单一物种 感染。然而，一个社区要想发挥作用，它必须是“稳健的”，因为它必须保留成员 物种，并在内部和外部的扰动中生存下来。社区健壮性源于交互 社区成员之间的关系，因此可以随着社区成员的发展而迅速变化。迄今为止，我们 对各种形式的健壮性以及它们在进化过程中如何变化知之甚少。理论上的 工作通常基于不切实际的假设，而经验工作主要是观察性的或相互关联的。 为了了解社区健壮性以及随着社区成员的发展，社区健壮性可能会如何变化，我们有 创建了一个合作的酵母社区。它由两个相互依赖的酵母菌株交换组成 必需的代谢物。这两个菌株是生殖性分离的，因此可以被认为是两个物种。 由于相互依赖，这两个菌株长期共存，因此可以进一步改造以 在降解混合废品等复杂任务中进行“分工”。然而，这样的一个 在人口减少的情况下，社区仍然可能灭绝。在这里，我们将检查社区的健壮性 两个经常遇到的外部扰动：极端的人口减少，如在 新寄主的定居，或种群的逐渐减少，例如在定期从肠道中清除的过程中。我们的目标是 理解这两种形式的健壮性，以便我们能够操纵它们。我们也想要了解 随着社区成员的发展和多样化，健壮性可能会发生怎样的变化。 我们已经走过了150多代人的多个社区。所有社区在年变得更加强大 在严重的人口减少中幸存下来。引人注目的是，对人口逐渐减少的抵抗力增强了 在一些社区，但在其他社区减少了。要了解社区的健壮性以及如何 它们在进化过程中会发生变化，我们已经开发出高通量的分析方法来测量来自 这两个菌株。基于这些测量的数学模型成功地预测了例如 对祖先社区中严重的人口减少的稳健性。我们将使用这些数学公式 预测我们可能如何有效地改变健壮性的模型。我们还将预测进化的哪一个子集 社区成员在改变社区健壮性方面非常重要。我们将对模型进行实验测试 预测。模型-实验的差异将激励我们找出对 群落稳健性，例如进化的新的相互作用和罕见的进化的基因类型与极端 表型。我们的工作将提供一种了解社区的实验和数学方法 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.