Linking dietary fiber to human malady through cumulative profiling of microbiota disturbance

IF 23.7 Q1 MICROBIOLOGY
iMeta Pub Date : 2025-02-19 DOI:10.1002/imt2.70004
Xin Zhang, Huan Liu, Yu Li, Yanlong Wen, Tianxin Xu, Chen Chen, Shuxia Hao, Jielun Hu, Shaoping Nie, Fei Gao, Gengjie Jia
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Abstract

Dietary fiber influences the composition and metabolic activity of microbial communities, impacting disease development. Current understanding of the intricate fiber-microbe-disease tripartite relationship remains fragmented and elusive, urging a systematic investigation. Here, we focused on microbiota disturbance as a robust index to mitigate various confounding factors and developed the Bio-taxonomic Hierarchy Weighted Aggregation (BHWA) algorithm to integrate multi-taxonomy microbiota disturbance data, thereby illuminating the complex relationships among dietary fiber, microbiota, and disease. By leveraging microbiota disturbance similarities, we (1) classified 32 types of dietary fibers into six functional subgroups, revealing correlations with fiber solubility; (2) established associations among 161 diseases, uncovering shared microbiota disturbance patterns that explain disease co-occurrence (e.g., type II diabetes and kidney diseases) and distinct microbiota patterns that discern symptomatically similar diseases (e.g., inflammatory bowel disease and irritable bowel syndrome); (3) designed a body-site-specific microbiota disturbance scoring scheme, computing a disturbance score (DS) for each disease and highlighting the pronounced capacity of Crohn's disease to disturb gut microbiota (DS = 14.01) in contrast with food allergy's minimal capacity (DS = 0.74); (4) identified 1659 fiber-disease associations, predicting the potential of dietary fiber to modulate specific microbiota changes associated with diseases of interest; (5) established murine models of inflammatory bowel disease to validate the preventive and therapeutic effects of arabinoxylan that notably perturbed the Bacteroidetes and Firmicutes phyla, as well as the Bacteroidetes and Lactobacillus genera, aligning with our model predictions. To enhance data accessibility and facilitate targeted dietary intervention development, we launched an interactive webtool—mDiFiBank at https://mdifibank.org.cn/.

Abstract Image

通过微生物群紊乱的累积分析将膳食纤维与人类疾病联系起来
膳食纤维影响微生物群落的组成和代谢活动,影响疾病的发展。目前对复杂的纤维-微生物-疾病三方关系的理解仍然是支离破碎和难以捉摸的,迫切需要系统的调查。本研究将微生物群干扰作为一种鲁棒性指标来减轻各种混杂因素,并开发了生物分类学层次加权聚合(BHWA)算法来整合多分类学微生物群干扰数据,从而阐明膳食纤维、微生物群和疾病之间的复杂关系。通过利用微生物群干扰的相似性,我们(1)将32种膳食纤维分为6个功能亚群,揭示了纤维溶解度的相关性;(2)建立了161种疾病之间的关联,揭示了解释疾病共发的共同微生物群紊乱模式(例如,II型糖尿病和肾脏疾病)和区分症状相似疾病的独特微生物群模式(例如,炎症性肠病和肠易激综合征);(3)设计了一种身体部位特异性微生物群干扰评分方案,计算每种疾病的干扰评分(DS),并突出克罗恩病对肠道微生物群的明显干扰能力(DS = 14.01),而食物过敏的最小干扰能力(DS = 0.74);(4)确定了1659种纤维与疾病的关联,预测了膳食纤维调节与相关疾病相关的特定微生物群变化的潜力;(5)建立小鼠炎症性肠病模型,验证阿拉伯木聚糖对拟杆菌门和厚壁菌门以及拟杆菌门和乳杆菌属的预防和治疗作用,与我们的模型预测一致。为了提高数据的可访问性和促进有针对性的饮食干预开发,我们在https://mdifibank.org.cn/上推出了一个交互式网络工具- mdifibank。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
10.80
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