University of California Davis, Department of Computer Science, 1 Shields Ave, Davis, CA, 95616, USA.
National Oceanic and Atmospheric Administration, Southwest Fisheries Science Center, 110 McAllister Way, Santa Cruz, CA, 95060, USA.
Nat Commun. 2020 Sep 28;11(1):4887. doi: 10.1038/s41467-020-18695-z.
The rise in the availability of bacterial genomes defines a need for synthesis: abstracting from individual taxa, to see larger patterns of bacterial lifestyles across systems. A key concept for such synthesis in ecology is the niche, the set of capabilities that enables a population's persistence and defines its impact on the environment. The set of possible niches forms the niche space, a conceptual space delineating ways in which persistence in a system is possible. Here we use manifold learning to map the space of metabolic networks representing thousands of bacterial genera. The results suggest a metabolic niche space comprising a collection of discrete clusters and branching manifolds, which constitute strategies spanning life in different habitats and hosts. We further demonstrate that communities from similar ecosystem types map to characteristic regions of this functional coordinate system, permitting coarse-graining of microbiomes in terms of ecological niches that may be filled.
从单个分类群中抽象出来,以在系统之间观察到更大的细菌生活方式模式。这种生态学综合的一个关键概念是生态位,它是一组使种群得以维持并定义其对环境影响的能力。可能的生态位集形成了生态位空间,这是一个概念空间,描绘了系统中持续存在的方式。在这里,我们使用流形学习来映射代表数千个细菌属的代谢网络空间。结果表明,代谢生态位空间由一系列离散的聚类和分支流形组成,这些聚类和分支流形构成了跨越不同栖息地和宿主的生活策略。我们进一步证明,来自相似生态系统类型的群落映射到这个功能坐标系的特征区域,允许根据可能被占据的生态位对微生物组进行粗化。