Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning
Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning
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DOI：
10.1016/j.isci.2020.101199
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发表时间：
2020-06-26
期刊：
影响因子：
5.8
通讯作者：
Zhavoronkov, Alex
中科院分区：
文献类型：
--
作者：
Galkin, Fedor;Mamoshina, Polina;Zhavoronkov, Alex
人类肠道微生物组是一个复杂的生态系统，既影响其宿主状态，又受到其宿主状态的影响。先前对肠道微生物区系的宏基因组分析揭示了特定微生物与宿主年龄之间的关联。尽管如此，还没有可靠的方法根据肠道菌群组成来判断宿主的年龄。在这里，我们开发了一种使用交叉研究数据集和深度学习根据微生物区系分类概况预测宿主年龄的方法。我们最好的模型具有深度神经网络架构，在外部数据上进行测试时，其平均绝对误差为 5.91 年。我们进一步推进了一种程序，用于推断特定微生物在人类衰老过程中的作用，并将它们定义为潜在的衰老生物标志物。所描述的肠道时钟代表了肠道微生物群衰老的独特定量模型，并为将宿主衰老和肠道群落演替构建为单一叙述提供了起点。
The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitativemodel of gutmicroflora aging and provides a starting point for building host aging and gut community succession into a single narrative.