Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences and Center for Microbiome, Nutrition, and Health, New Jersey Institute for Food, Nutrition, and Health, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA; Rutgers-Jiaotong Joint Laboratory for Microbiome and Human Health, New Brunswick, NJ, USA.
State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Cell. 2024 Nov 14;187(23):6550-6565.e11. doi: 10.1016/j.cell.2024.09.019. Epub 2024 Oct 7.
The gut microbiota is crucial for human health, functioning as a complex adaptive system akin to a vital organ. To identify core health-relevant gut microbes, we followed the systems biology tenet that stable relationships signify core components. By analyzing metagenomic datasets from a high-fiber dietary intervention in type 2 diabetes and 26 case-control studies across 15 diseases, we identified a set of stably correlated genome pairs within co-abundance networks perturbed by dietary interventions and diseases. These genomes formed a "two competing guilds" (TCGs) model, with one guild specialized in fiber fermentation and butyrate production and the other characterized by virulence and antibiotic resistance. Our random forest models successfully distinguished cases from controls across multiple diseases and predicted immunotherapy outcomes through the use of these genomes. Our guild-based approach, which is genome specific, database independent, and interaction focused, identifies a core microbiome signature that serves as a holistic health indicator and a potential common target for health enhancement.
肠道微生物群对人类健康至关重要，它作为一个复杂的自适应系统，类似于一个重要的器官。为了确定与健康相关的核心肠道微生物，我们遵循系统生物学的原则，即稳定的关系意味着核心成分。通过分析高纤维饮食干预 2 型糖尿病和 26 项疾病对照研究的宏基因组数据集，我们在饮食干预和疾病干扰的共丰度网络中确定了一组稳定相关的基因组对。这些基因组形成了一个“两个竞争的群体”（TCGs）模型，一个群体专门从事纤维发酵和丁酸产生，另一个群体以毒力和抗生素耐药性为特征。我们的随机森林模型通过使用这些基因组，成功地区分了多种疾病的病例和对照，并预测了免疫治疗的结果。我们的基于群体的方法是针对基因组的，不依赖于数据库，并且侧重于相互作用，它确定了一个核心微生物组特征，作为一个整体健康指标和增强健康的潜在共同目标。
