Dietary fibers are generally thought to benefit intestinal health. Their impacts on the composition and metabolic function of the gut microbiome, however, vary greatly across individuals. Previous research showed that each individual’s response to fibers depends on their baseline gut microbiome, but the ecology driving microbiota remodeling during fiber intake remained unclear. Here, we studied the long-term dynamics of the gut microbiome and short-chain fatty acids (SCFAs) in isogenic mice with distinct microbiota baselines fed with the fermentable fiber inulin and resistant starch compared to the non-fermentable fiber cellulose. We found that inulin produced a generally rapid response followed by gradual stabilization to new equilibria, and those dynamics were baseline-dependent. We parameterized an ecology model from the time-series data, which revealed a group of bacteria whose growth significantly increased in response to inulin and whose baseline abundance and interspecies competition explained the baseline dependence of microbiome density and community composition dynamics. Fecal levels of SCFAs, such as propionate, were associated with the abundance of inulin responders, yet inter-individual variation of gut microbiome impeded the prediction of SCFAs by machine learning models. We showed that our methods and major findings were generalizable to dietary resistant starch. Finally, we analyzed time-series data of synthetic and natural human gut microbiome in response to dietary fiber and validated the inferred interspecies interactions in vitro. This study emphasizes the importance of ecological modeling to understand microbiome responses to dietary changes and the need for personalized interventions.

人们普遍认为膳食纤维有益于肠道健康。然而，它们对肠道微生物组的组成和代谢功能的影响因人而异。先前的研究表明，每个人对纤维的反应取决于他们的基线肠道微生物群，但纤维摄入过程中驱动微生物群重塑的生态仍不清楚。在这里，我们研究了与非发酵纤维纤维素喂养相比，饲喂可发酵纤维菊粉和抗性淀粉的同基因小鼠肠道微生物组和短链脂肪酸 (SCFA) 的长期动态，这些小鼠具有不同的微生物群基线。我们发现菊粉产生普遍快速的反应，然后逐渐稳定到新的平衡，并且这些动态是基线依赖性的。我们根据时间序列数据参数化了一个生态模型，该模型揭示了一组细菌的生长响应菊粉而显着增加，其基线丰度和种间竞争解释了微生物组密度和群落组成动态的基线依赖性。粪便中丙酸等 SCFA 的水平与菊粉反应者的丰度相关，但肠道微生物组的个体间差异阻碍了机器学习模型对 SCFA 的预测。我们表明，我们的方法和主要发现可以推广到膳食抗性淀粉。最后，我们分析了合成和天然人类肠道微生物组对膳食纤维的反应的时间序列数据，并在体外验证了推断的种间相互作用。这项研究强调了生态模型对于了解微生物组对饮食变化的反应以及个性化干预的必要性的重要性。