Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis

ABSTRACT

Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.

KEYWORDS:

• Genome-scale metabolic model
• machine learning
• biomarker selection
• inflammatory bowel disease
• ulcerative colitis
• Parabacteroides merdae ATCC 43184
• N-acetyl-D-mannosamine

Disclosure statement

No potential conflict of interest was reported by the authors.

Availability of data

The data presented in this study are available in the NCBI BioProject under accession number PRJNA398089.

Author contributions

Conceptualization: J.Z., W.L. and H.W.; Formal Analysis: J.Z. and H.Z.; Funding acquisition: J.Z. and W.C.; Investigation: J.Z., H.Z., W.L. and H.W.; Methodology: J.Z., J.Y., W.L. and H.W.; Project administration: H.Z.; Resources: W.L.; Supervision: H.Z. and W.L.; Validation: J.Z. and J.Y.; Visualization: J.Y.; Writing-original draft: J.Z. and J.Y.; Other assistance: M.H. and J.C.; Writing-review and editing: W.C. All authors have read and agreed to the published version of the manuscript.

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2024.2336877.

Additional information

Funding

This research was funded in part by grants from National Natural Science Foundation of China (No. 32372345), Fundamental Research Funds for the Central Universities [JUSRP622034], National Natural Science Foundation of China [No. 32021005, No. 31820103010], and Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province.