Functional attractors in microbial community assembly.

Functional attractors in microbial community assembly.
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微生物社区大会中的功能吸引子。

DOI:
10.1016/j.cels.2021.09.011
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发表时间:
2022-01-19
期刊:
影响因子:
9.3
通讯作者:
Sánchez Á
Sánchez Á
中科院分区:
生物学1区
文献类型:
--
作者:
Estrela S;Vila JCC;Lu N;Bajić D;Rebolleda-Gómez M;Chang CY;Goldford JE;Sanchez-Gorostiaga A;Sánchez Á

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为了使微生物组生物学成为一门更具预测性的科学,我们必须确定微生物群落的哪些描述特征是可复制和可预测的,哪些不可复制和可预测,以及为什么。我们通过实验研究复制的葡萄糖有限的生境中微生物群落组装中的并行性和趋同性来解决这个问题。在这里,我们表明,以前在这些栖息地观察到的家族水平的趋同反映了一种可复制的代谢组织,其中主要代谢组的比例可以用一个简单的资源分配模型来解释。反过来,复制群落之间的分类学差异是由于种群动态的多稳定性造成的。在封闭的生态系统中,多重稳定性也可以导致另一种功能状态,但在元群落中则不会。我们的发现经验性地说明了数量代谢特征的进化保守性、多稳定性和种群动态固有的随机性,这些都可能共同产生微生物群落组装中常见的不同组织水平上的重复性和可变性模式。微生物群可以在不同的组织层次上描述:从菌株到代谢功能。随着我们缩小视野,观察微生物组的新兴功能行为,微生物组组装的可预测性往往会增加。由于在其自然栖息地研究微生物群的巨大挑战,这些反复出现的模式仍然知之甚少。在这里,我们调查了实验室生态系统,尽管在分类上存在细微的分歧,但它们表现出类似的功能趋同模式。通过实验和建模相结合的方法,我们给出了这些模式的机理解释。
For microbiome biology to become a more predictive science, we must identify which descriptive features of microbial communities are reproducible and predictable, which are not, and why. We address this question by experimentally studying parallelism and convergence in microbial community assembly in replicate glucose-limited habitats. Here, we show that the previously observed family-level convergence in these habitats reflects a reproducible metabolic organization, where the ratio of the dominant metabolic groups can be explained from a simple resource-partitioning model. In turn, taxonomic divergence among replicate communities arises from multistability in population dynamics. Multistability can also lead to alternative functional states in closed ecosystems but not in metacommunities. Our findings empirically illustrate how the evolutionary conservation of quantitative metabolic traits, multistability, and the inherent stochasticity of population dynamics, may all conspire to generate the patterns of reproducibility and variability at different levels of organization that are commonplace in microbial community assembly. Microbiomes may be described at different levels of organization: from strains to metabolic functions. The predictability of microbiome assembly often increases as we zoom out and look at their emergent functional behavior. Due to the significant challenges of studying microbiomes in their natural habitats, these recurrent patterns remain poorly understood. Here, we investigate laboratory ecosystems exhibiting a similar pattern of functional convergence despite fine-scale taxonomic divergence. By combining experiments and modeling, we provide a mechanistic explanation for these patterns.
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