The environmental metabolome and metabolic potential of microorganisms are dominant and essential factors shaping microbial community composition. Recent advances in genome annotation and systems biology now allow us to semiautomatically reconstruct genome-scale metabolic models (GSMMs) of microorganisms based on their genome sequence ¹ . Next, growth of these models in a defined metabolic environment can be predicted in silico, mechanistically linking the metabolic fluxes of individual microbial populations to the community dynamics. A major advantage of GSMMs is that no training data is needed, besides information about the metabolic capacity of individual genes (genome annotation) and knowledge of the available environmental metabolites that allow the microorganism to grow. However, the composition of the environment is often not fully determined and remains difficult to measure ² . We hypothesized that the relative abundance of different bacterial species, as measured by metagenomics, can be combined with GSMMs of individual bacteria to reveal the metabolic status of a given biome. Using a newly developed algorithm involving over 1,500 GSMMs of human-associated bacteria, we inferred distinct metabolomes for four human body sites that are consistent with experimental data. Together, we link the metagenome to the metabolome in a mechanistic framework towards predictive microbiome modelling.

中文翻译：

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通过机械模型从宏基因组学预测环境代谢组学。

微生物的环境代谢组和代谢潜力是决定微生物群落组成的主要因素和必要因素。基因组注释和系统生物学的最新进展现在使我们能够基于微生物的基因组序列¹半自动重建微生物的基因组规模代谢模型（GSMM）。。接下来，可以在计算机上预测这些模型在定义的代谢环境中的增长，从而将单个微生物种群的代谢通量与社区动态联系起来。GSMM的主要优点是，除了有关单个基因的代谢能力的信息（基因组注释）以及可让微生物生长的可用环境代谢物的知识外，无需培训数据。但是，环境的组成通常无法完全确定，仍然难以衡量²。我们假设通过宏基因组学测量的不同细菌种类的相对丰度可以与单个细菌的GSMM结合起来，揭示给定生物群系的代谢状态。使用一种新开发的算法，该算法涉及超过1,500个GSMM的人类相关细菌，我们推断出四个人体部位的独特代谢组，这些代谢组与实验数据一致。我们共同将代谢基因组与代谢组联系起来，并建立了可预测微生物组建模的机制。