Department of Biology, Duke University, Durham, NC, USA.
Research School of Biology, The Australian National University, Canberra, ACT, Australia.
Microbiome. 2018 Apr 27;6(1):80. doi: 10.1186/s40168-018-0464-x.
Most empirical studies tend to focus on microbiome dynamics within hosts or microbiome compositional differences between hosts over short periods. However, there is still a dearth of formal models that allow us to investigate the observed short-term dynamics of microbiomes under a unified ecological and evolutionary framework. In our previous study, we developed a computational agent-based neutral framework that simulates microbiome dynamics spanning many host generations with the added dimension of a genealogy of hosts. Although this long-term framework revealed interesting microbial diversity patterns under a simple but plausible evolutionary process and provided a platform for future elaboration of more complex systems, it does not allow us to explore microbiome dynamics within a single host generation.
In this paper, we developed a computational, agent-based, forward-time framework of microbiome dynamics within a single host generation. As we have done under our neutral long-term models, we incorporate neutral processes of environmental microbiome assembly and microbe acquisition from parents and environment. We also incorporate a Moran genealogical model of hosts, so that the dynamics of microbiome evolution can be studied within a single host generation. Furthermore, we allow host subpopulation structure and host migration to affect microbiome recruitment.
We show that microbiome diversity within hosts increases monotonically with increases in environmental contribution, while microbiome diversity between hosts increases with increasing parental inheritance. Host population division and dispersal limitation under high host contribution further shaped the patterns by elevating microbiome differences between hosts and depressing microbial diversity within hosts. Microbiome diversity within the whole population showed strong temporal stability regardless of the modes of microbiome acquisition and subpopulation structures.
We present a computational framework that integrates various processes including host genealogy, microbe recruitment, and host dispersal limitation acting on the short-term dynamics of microbiomes. Our framework demonstrates that the neutral dynamics of microbiomes within a population of hosts is strongly influenced by transmission mode and shared environment.
大多数经验性研究倾向于关注宿主内微生物组的动态或宿主间微生物组组成的差异,且研究时间都较短。然而,目前仍然缺乏正式的模型,使我们能够在统一的生态和进化框架下研究微生物组的短期动态。在我们之前的研究中,我们开发了一种基于计算代理的中性框架,该框架模拟了跨越多个宿主代的微生物组动态,并增加了宿主谱系这一维度。虽然这种长期框架揭示了在简单但合理的进化过程下微生物多样性的有趣模式,并为未来更复杂系统的阐述提供了平台,但它不允许我们在单个宿主代内探索微生物组的动态。
在本文中,我们开发了一种在单个宿主代内进行微生物组动态的基于计算代理的正向时间框架。与我们在中性长期模型中所做的一样,我们整合了环境微生物组组装和从父母和环境中获取微生物的中性过程。我们还整合了宿主的 Moran 谱系模型,以便在单个宿主代内研究微生物组进化的动态。此外,我们允许宿主亚群结构和宿主迁移来影响微生物组的招募。
我们表明,宿主内微生物组的多样性随着环境贡献的增加而单调增加,而宿主间微生物组的多样性则随着亲本遗传的增加而增加。在高宿主贡献下的宿主种群分裂和扩散限制进一步通过提高宿主间微生物组的差异和降低宿主内微生物的多样性来塑造这些模式。无论微生物组获取和亚群结构的模式如何,整个种群的微生物组多样性都表现出很强的时间稳定性。
我们提出了一个计算框架,该框架整合了各种过程,包括宿主谱系、微生物招募和宿主扩散限制,以研究微生物组的短期动态。我们的框架表明,宿主群体中微生物组的中性动态强烈受到传播模式和共享环境的影响。