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

Area of Science:

• Microbiome research
• Nutritional science
• Computational biology

Background:

• Dietary fiber significantly influences gut microbial communities and their metabolic activities, playing a crucial role in disease development.
• The complex interplay between dietary fiber, the gut microbiota, and disease pathogenesis is not fully understood, necessitating systematic investigation.

Purpose of the Study:

• To systematically investigate the tripartite relationship between dietary fiber, microbiota, and disease.
• To develop a novel algorithm for integrating multi-taxonomy microbiota disturbance data to illuminate these complex relationships.
• To create a predictive framework for dietary fiber interventions targeting microbiota-associated diseases.

Main Methods:

• Developed the Bio-taxonomic Hierarchy Weighted Aggregation (BHWA) algorithm to analyze microbiota disturbance data.
• Classified 32 dietary fibers into six functional subgroups based on microbiota disturbance similarities.
• Established associations between 161 diseases and their microbiota disturbance patterns.
• Created a body-site-specific microbiota disturbance scoring scheme.
• Identified 1659 fiber-disease associations and validated findings in murine models of inflammatory bowel disease.

Main Results:

• Dietary fibers were functionally classified, correlating with solubility.
• Shared and distinct microbiota disturbance patterns were identified for 161 diseases, explaining disease co-occurrence and differentiation.
• A microbiota disturbance score (DS) was computed for diseases, with Crohn's disease showing high impact (DS=14.01) and food allergy showing minimal impact (DS=0.74).
• 1659 fiber-disease associations were identified, predicting potential dietary interventions.
• Arabinoxylan demonstrated preventive and therapeutic effects in inflammatory bowel disease models, altering specific bacterial phyla and genera as predicted.

Conclusions:

• The BHWA algorithm effectively integrates multi-taxonomy microbiota data to elucidate the fiber-microbe-disease axis.
• This systematic approach facilitates the classification of dietary fibers, understanding disease associations, and predicting targeted interventions.
• The developed interactive webtool, mDiFiBank, enhances data accessibility for developing targeted dietary interventions.