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Abstract: FR-PO002

Integrative Metagenomic, Metabolomic, and Deep Immune Profiling Reveal Coordinate Effects on Host-Microbe Interactions in CKD

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Author

  • Wu, I-Wen, Chang Gung Memorial Hospital, Keelung, Keelung, Taiwan
Background

Perturbation of gut dysbiosis is present in chronic kidney disease (CKD) and associated with a sophisticated milieu of metabolic and immune dysregulation. However, the underlying host-microbe interaction is unclear.

Methods

We performed multi-omics measurements, including systems-level gut microbiome, targeted serum metabolome, and high-dimensional immunotyping, in a cohort of 72 CKD patients and 20 controls.

Results

Our analyses on functional profiles of gut microbiome showed that loss of renal function decreased the diversity and abundance of carbohydrate-active enzyme (CAZyme) genes, but increased the abundance of antibiotic resistance, nitrogen cycling enzyme, and virulence factor genes. Models generated using measurements of circulating metabolites (amino acids, bile acids, and short-chain fatty acids) or immunotypes were predictive of renal impairment but less so than many of the taxonomic or functional profiles derived from gut microbiota, with the CAZyme genes being the top performing model to accurately predict early stage of diseases. Correlation analyses among different omics parameters revealed coordinated host-microbe relationships in CKD. Specifically, significant correlations were identified with circulating metabolites by several taxonomic and functional profiles of gut microbiome, while immunotype features were only moderately associated with the abundance of microbiome-encoded metabolic pathways and serum levels of amino acids.

Conclusion

Our multi-omics integration revealed signatures of the systems-level gut microbiome in robust associations with host-microbe co-metabolites and renal function, highlighting potential etiological and diagnostic implications in CKD.

Integration of the multi-omic experiment (A) The types and sample sources of omics analyses. (B) α- and β-diversity among the groups. (C) t-SNE plot detected by the flow cytometer. (D) PBMCs abundance densities from the groups. (E) Correlations between multi-omic data types and renal function.

Funding

  • Private Foundation Support