Ecological dynamics of the gut microbiome in response to dietary fiber

• Published in: The ISME Journal 2022-08, Vol.16 (8), p.2040-2055
• Main Authors: Liu, Hongbin, Liao, Chen, Wu, Lu, Tang, Jinhui, Chen, Junyu, Lei, Chaobi, Zheng, Linggang, Zhang, Chenhong, Liu, Yang-Yu, Xavier, Joao, Dai, Lei
• Format: Article
• Language: English
• Subjects: 45/23; 631/158/855; 631/326/2565/2134; 64/60; Abundance; Animals; Biomedical and Life Sciences; Cellulose; Cellulose fibers; Community composition; Composition; Dietary fiber; Dietary Fiber - metabolism; Digestive system; Dynamics; Ecological models; Ecology; Evolutionary Biology; Fatty acids; Fatty Acids, Volatile - metabolism; Feces - microbiology; Fibers; Gastrointestinal Microbiome; Gastrointestinal tract; Humans; Intestinal microflora; Inulin; Life Sciences; Machine learning; Mice; Microbial Ecology; Microbial Genetics and Genomics; Microbiology; Microbiomes; Microbiota; Propionic acid; Resistant Starch; Starch; Time series
• ISSN: 1751-7362; 1751-7370; 1751-7370
• DOI: 10.1038/s41396-022-01253-4

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.