PDF(Pubmed)
Abstract：
The human gut microbiota comprises various microorganisms engaged in intricate interactions among themselves and with the host, affecting its health. While advancements in omics technologies have led to the inference of clear associations between microbiome composition and health conditions, we usually lack a causal and mechanistic understanding of these associations. For modeling mechanisms driving the interactions, we simulated the organism\'s metabolism using in silico genome-scale metabolic models (GEMs). We used multi-objective optimization to predict and explain metabolic interactions among gut microbes and an intestinal epithelial cell. We developed a score integrating model simulation results to predict the type (competition, neutralism, mutualism) and quantify the interaction between several organisms. This framework uncovered a potential cross-feeding for choline, explaining the predicted mutualism between Lactobacillus rhamnosus GG and the epithelial cell. Finally, we analyzed a five-organism ecosystem, revealing that a minimal microbiota can favor the epithelial cell\'s maintenance.
摘要：
人类肠道微生物群包括各种微生物，它们之间以及与宿主之间进行复杂的相互作用，影响其健康。虽然组学技术的进步导致了微生物组组成与健康状况之间明确关联的推断，我们通常缺乏对这些关联的因果和机械理解。对于驱动交互的建模机制，我们使用计算机基因组尺度代谢模型(GEM)模拟生物体的代谢。我们使用多目标优化来预测和解释肠道微生物和肠上皮细胞之间的代谢相互作用。我们开发了一个集成模型模拟结果的分数来预测类型(竞争，中立主义,互利)并量化几种生物之间的相互作用。这个框架揭示了胆碱的潜在交叉喂养，解释了鼠李糖乳杆菌GG与上皮细胞之间的预测共生关系。最后,我们分析了一个五生物生态系统，揭示了最小的微生物群可以有利于上皮细胞的维持。
