Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Ecol Evol. 2023 May;7(5):716-724. doi: 10.1038/s41559-023-02021-z. Epub 2023 Mar 30.
Recent studies have shown that microbial communities are composed of groups of functionally cohesive taxa whose abundance is more stable and better-associated with metabolic fluxes than that of any individual taxon. However, identifying these functional groups in a manner that is independent of error-prone functional gene annotations remains a major open problem. Here we tackle this structure-function problem by developing a novel unsupervised approach that coarse-grains taxa into functional groups, solely on the basis of the patterns of statistical variation in species abundances and functional read-outs. We demonstrate the power of this approach on three distinct datasets. On data of replicate microcosms with heterotrophic soil bacteria, our unsupervised algorithm recovered experimentally validated functional groups that divide metabolic labour and remain stable despite large variation in species composition. When leveraged against the ocean microbiome data, our approach discovered a functional group that combines aerobic and anaerobic ammonia oxidizers whose summed abundance tracks closely with nitrate concentrations in the water column. Finally, we show that our framework can enable the detection of species groups that are probably responsible for the production or consumption of metabolites abundant in animal gut microbiomes, serving as a hypothesis-generating tool for mechanistic studies. Overall, this work advances our understanding of structure-function relationships in complex microbiomes and provides a powerful approach to discover functional groups in an objective and systematic manner.
最近的研究表明，微生物群落由功能上有凝聚力的分类群组成，其丰度比任何单个分类群更稳定，与代谢通量的相关性更好。然而，以独立于易错功能基因注释的方式识别这些功能群仍然是一个主要的未解决问题。在这里，我们通过开发一种新颖的无监督方法来解决这个结构-功能问题，该方法仅根据物种丰度和功能读出的统计变化模式，将分类群粗分为功能群。我们在三个不同的数据集上证明了这种方法的有效性。在含有异养土壤细菌的重复微宇宙数据上，我们的无监督算法恢复了经过实验验证的功能群，这些功能群划分了代谢工作，并且尽管物种组成有很大变化，但仍然保持稳定。当利用海洋微生物组数据时，我们的方法发现了一个将好氧氨氧化菌和厌氧氨氧化菌结合在一起的功能群，其总丰度与水柱中的硝酸盐浓度密切相关。最后，我们表明，我们的框架可以检测可能负责动物肠道微生物组中丰富代谢物的产生或消耗的物种群，为机制研究提供了一个生成假设的工具。总的来说，这项工作推进了我们对复杂微生物组中结构-功能关系的理解，并提供了一种以客观和系统的方式发现功能群的强大方法。
