Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk 37673, Republic of Korea.
Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA.
Nat Commun. 2017 Jun 6;8:15393. doi: 10.1038/ncomms15393.
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.
一个复杂的微生物-微生物和宿主-微生物化学交叉对话的系统级框架将有助于阐明我们肠道微生物群在健康和疾病中的作用。在这里,我们报告了一个人类肠道微生物群的文献编纂种间网络,称为 NJS16。这是一个广泛的数据资源,由大约 570 种微生物物种和 3 种人类细胞类型组成,通过 >4400 个小分子运输和大分子降解事件进行代谢相互作用。基于我们网络的内容,我们开发了一种数学方法来阐明给定人群(如疾病队列)中肠道微生物群落的代表性微生物和代谢特征。将这一策略应用于 2 型糖尿病患者的微生物组数据,揭示了肠道微生物生态系统的特定于上下文的基础设施、具有较大代谢影响的核心微生物实体,以及可能表明相关社区代谢过程的频繁产生的代谢化合物。我们的网络为在人类肠道内进行社区规模的微生物活动的综合研究提供了基础。