从横断面数据预测肥胖个体的肠道微生物群动态变化。
Predicting gut microbiota dynamics in obese individuals from cross-sectional data.
作者信息
Melvan Ena, Allen Andrew P, Vuckovic Tea, Soljic Irena, Starcevic Antonio
机构信息
School of Natural Sciences, Macquarie University, Sydney, NSW, Australia.
Research and Development Department, Metabelly, Split, Croatia.
出版信息
Front Cell Infect Microbiol. 2025 Jun 10;15:1485791. doi: 10.3389/fcimb.2025.1485791. eCollection 2025.
INTRODUCTION
Obesity affects approximately 39% of adults worldwide. While gut microbiota has been linked to obesity, most research has focused on static taxonomic composition rather than the dynamic interactions between microbial taxa.
METHODS
We applied BEEM-Static, a generalized Lotka-Volterra model, to cross-sectional 16S rRNA gut microbiome data from six public datasets, comprising 2,435 profiles from lean and obese individuals.
RESULTS
A total of 57 significant microbial interactions were identified in obese individuals (79% negative), compared to 37 in lean individuals (92% negative). For example, Bacteroidetes showed a stronger inhibitory effect on Firmicutes in obese individuals (-0.41) than in lean ones (-0.26). Firmicutes and Proteobacteria exhibited consistently higher carrying capacities in obese populations.
DISCUSSION
These findings suggest that microbial interaction networks-not just taxonomic abundance-play a key role in obesity-related dysbiosis. Our approach enables the inference of microbiota dynamics from a single time point, paving the way for tailored dietary interventions, which we refer to as .
引言
肥胖影响着全球约39%的成年人。虽然肠道微生物群与肥胖有关，但大多数研究都集中在静态的分类组成上，而不是微生物分类群之间的动态相互作用。
方法
我们应用广义洛特卡-沃尔泰拉模型BEEM-Static，对来自六个公共数据集的横断面16S rRNA肠道微生物组数据进行分析，这些数据集包含2435份瘦人和肥胖个体的样本。
结果
在肥胖个体中总共鉴定出57种显著的微生物相互作用（79%为负相互作用），而在瘦个体中为37种（92%为负相互作用）。例如，拟杆菌门对肥胖个体中厚壁菌门的抑制作用（-0.41）比瘦个体中（-0.26）更强。厚壁菌门和变形菌门在肥胖人群中的承载能力一直较高。
讨论
这些发现表明，微生物相互作用网络——而不仅仅是分类丰度——在肥胖相关的生态失调中起关键作用。我们的方法能够从单个时间点推断微生物群动态，为量身定制的饮食干预铺平了道路，我们将其称为 。
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