Data from: Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis

A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.

一套用于解析复杂微生物间、宿主与微生物间化学信号交流的系统级框架，将有助于阐明肠道菌群（gut microbiota）在人体健康与疾病进程中所发挥的作用。本研究报道了一个经文献整理的人类肠道菌群跨物种互作网络，命名为NJS16。该数据集规模庞大，涵盖约570种微生物物种与3种人类细胞类型，二者通过超过4400个小分子转运及大分子降解事件实现代谢互作。基于本网络的数据集内容，我们开发了一种数学分析方法，用于解析特定人群（如疾病队列）中肠道菌群群落的典型微生物与代谢特征。将该策略应用于2型糖尿病患者的微生物组（microbiome）数据后，我们得以揭示肠道微生物生态系统的情境特异性架构、具备强代谢影响力的核心微生物类群，以及可能反映群落核心代谢过程的高频产生代谢产物。本网络为人类肠道内群落尺度微生物活动的整合性研究提供了重要基础。