Advances in understanding dietary fiber: Classification, structural characterization, modification, and gut microbiome interactions

肠道菌群 膳食纤维 微生物群 生物 肠道微生物群 计算生物学 食品科学 生物化学 生物信息学
作者
Yiming Feng,Qing Jin,Xuanbo Liu,Tiantian Lin,Andrea Johnson,Haibo Huang
出处
期刊:Comprehensive Reviews in Food Science and Food Safety [Wiley]
卷期号:24 (1)
标识
DOI:10.1111/1541-4337.70092
摘要

Abstract Gut microbiota and their metabolites profoundly impact host physiology. Targeted modulation of gut microbiota has been a long‐term interest in the scientific community. Numerous studies have investigated the feasibility of utilizing dietary fibers (DFs) to modulate gut microbiota and promote the production of health‐beneficial bacterial metabolites. However, the complexity of fiber structures, microbiota composition, and their dynamic interactions have hindered the precise prediction of the impact of DF on the gut microbiome. We address this issue with a new perspective, focusing on the inherent chemical and structural complexity of DFs and their interaction with gut microbiota. The chemical and structural complexity of fibers was thoroughly elaborated, encompassing the fibers’ molecular composition, polymorphism, mesoscopic structures, porosity, and particle size. Advanced characterization techniques to investigate fiber structural properties were discussed. Additionally, we examined the interactions between DFs and gut microbiota. Finally, we summarized processing techniques to modify fiber structures for improving the fermentability of DF by gut microbiota. The structure of fibers, such as their crystallinity, porosity, degree of branching, and pore wettability, significantly impacts their interactions with gut microbiota. These structural differences also substantially affect fiber's fermentability and capability to modulate the composition of gut microbiota. Conventional approaches are not capable of investigating complex fiber properties and their influences on the gut microbiome; therefore, it is of the essence to involve advanced material characterization techniques and artificial intelligence to unveil more comprehensive information on this topic.
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