Dynamic coexistence driven by physiological transitions in microbial communities.
Dynamic coexistence driven by physiological transitions in microbial communities.
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由微生物群落的生理转变驱动的动态共存。
DOI：
10.1101/2024.01.10.575059
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Murugan,Arvind
中科院分区：
文献类型：
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作者：
Narla,AvaneeshV;Hwa,Terence;Murugan,Arvind
微生物生态系统通常通过稳定的指数增长状态下物种之间的固定相互作用来建模。然而，呈指数增长的微生物经常会强烈地改变它们的环境，以至于它们被迫从生长状态进入压力状态，不生长状态。这种动态是自然界生态演替和实验室连续稀释循环的典型特征。在这里，我们引入了一个现象学模型，群落状态模型，以深入了解微生物在循环演替过程中由于生理状态的变化而动态共存。我们的模型指定了每个物种沿着全球生态坐标的生长偏好，即群落的生物量密度，但对特定的相互作用（例如，营养饥饿，压力，聚集）不可知，以便关注生理状态组合的自一致性条件，“群落状态”，在一个稳定的生态系统中。我们发现动态群落与稳态群落形成鲜明对比的三个关键特征：通过不同物种在不同群落状态下的交错优势来增强群落稳定性，提高群落多样性对快速生长物种在不同群落状态下占主导地位的容忍度，以及增加生长较晚的物种对生长优势的要求。这些特征是为简化模型明确推导出来的，在这里作为帮助理解复杂动态群落的原则提出。我们的模型将生态系统动力学的重点从基于固定的、理想化的种间相互作用的自下而上的研究转移到基于可获得的宏观可观测数据（如增长率和总生物量密度）的自上而下的研究，从而能够定量研究整个群落的特征。
Microbial ecosystems are commonly modeled by fixed interactions between species in steady exponential growth states. However, microbes in exponential growth often modify their environments so strongly that they are forced out of the growth state into stressed, nongrowing states. Such dynamics are typical of ecological succession in nature and serial-dilution cycles in the laboratory. Here, we introduce a phenomenological model, the Community State Model, to gain insight into the dynamic coexistence of microbes due to changes in their physiological states during cyclic succession. Our model specifies the growth preference of each species along a global ecological coordinate, taken to be the biomass density of the community, but is otherwise agnostic to specific interactions (e.g., nutrient starvation, stress, aggregation), in order to focus on self-consistency conditions on combinations of physiological states, “community states,” in a stable ecosystem. We identify three key features of such dynamical communities that contrast starkly with steady-state communities: enhanced community stability through staggered dominance of different species in different community states, increased tolerance of community diversity to fast growing species dominating distinct community states, and increased requirement of growth dominance by late-growing species. These features, derived explicitly for simplified models, are proposed here as principles aiding the understanding of complex dynamical communities. Our model shifts the focus of ecosystem dynamics from bottom–up studies based on fixed, idealized interspecies interaction to top–down studies based on accessible macroscopic observables such as growth rates and total biomass density, enabling quantitative examination of community-wide characteristics.