Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Nat Commun. 2021 Feb 26;12(1):1335. doi: 10.1038/s41467-021-21586-6.
Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combine metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotations, which we provide for experimental testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.
理解像人类肠道微生物组这样复杂的微生物生态系统需要了解微生物物种以及它们产生和分泌的代谢物的信息。这些代谢物通过大量的交叉喂养相互作用进行交换，对于预测微生物组的功能状态至关重要。然而，到目前为止，我们只获得了这个网络的一部分信息，受到实验通量的限制。在这里，我们提出了一种基于生态学的计算方法 GutCP，利用该方法我们预测了人类肠道微生物组中数百种新的、未经实验测试的交叉喂养相互作用。GutCP 利用肠道微生物组的机制模型，明确地交换代谢物及其对微生物物种生长的影响。为了构建 GutCP，我们将肠道微生物组的宏基因组和代谢组测量数据与机器学习中的优化技术相结合。GutCP 预测的近 65%的交叉喂养相互作用得到了来自基因组注释的证据支持，我们为此提供了实验测试的依据。我们的方法有可能极大地改进现有的人类肠道微生物组模型，以及我们预测肠道代谢特征的能力。
