Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel.
The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
Microbiome. 2024 Feb 10;12(1):24. doi: 10.1186/s40168-023-01737-1.
The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.
We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).
These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.
微生物对其人类宿主的影响通常是通过改变代谢物浓度来介导的。因此，已经提出了多种工具来预测微生物分类群频率与代谢物浓度之间的关系。这些工具通常无法捕捉微生物组-代谢物关系对环境的依赖。
我们提出将微生物组-代谢组关系视为复杂相互作用的平衡，并将宿主状况与代谢组对数浓度与微生物组对数频率之间相互作用的潜在表示相关联。我们开发了 LOCATE（微生物组和代谢物关系的潜在变量），这是一种机器学习工具，可从微生物组组成预测代谢物浓度，并产生微生物组和代谢组之间相互作用的潜在表示。然后，该表示用于预测宿主状况。LOCATE 在预测代谢物方面的准确性高于所有当前预测器。代谢物浓度预测精度在跨数据集和跨条件下显著降低，特别是在 16S 数据中。LOCATE 的潜在表示比微生物组或代谢物更好地预测宿主状况。该表示与宿主人口统计学高度相关。即使只有少量代谢物样本（[公式：见正文]），也可以获得显著的准确性提高（0.793 与 0.724 的平均准确性）。
这些结果表明，微生物组-代谢组相互作用的潜在表示与宿主状况的关联优于任何两种单独的表示或两者的简单组合。
