Multi-omic integration of microbiome data for identifying disease-associated modules.

Area of Science:

• Microbiome research
• Systems biology
• Computational biology

Background:

• Multi-omic studies of the human gut microbiome are vital for understanding disease.
• Integrating and analyzing complex multi-omic data presents significant challenges.
• Current methods often fail to capture the multi-layered structure of microbiome-disease associations.

Purpose of the Study:

• To introduce MintTea, an intermediate integration approach for analyzing multi-omic gut microbiome data.
• To identify disease-associated multi-omic modules that capture concordant shifts in features across different omics layers.
• To generate systems-level hypotheses for microbiome-disease interactions.

Main Methods:

• MintTea combines extensions of canonical correlation analysis, consensus analysis, and an evaluation protocol.
• The approach identifies modules comprising features from multiple omics that shift in concert and associate with disease.
• Applied to diverse cohorts, including metabolic syndrome and colorectal cancer datasets.

Main Results:

• MintTea successfully identifies disease-associated multi-omic modules with high predictive power.
• Modules show significant cross-omic correlations and align with known microbiome-disease associations.
• Specific modules linked to metabolic syndrome (glutamate, TCA cycle metabolites, insulin resistance-linked bacteria) and colorectal cancer (bacterial species, fecal amino acids) were identified.

Conclusions:

• MintTea offers an advanced integration method for uncovering multifaceted microbiome-disease interactions.
• The identified modules provide systems-level insights into the complex interplay between the gut microbiome and disease.
• This approach facilitates the generation of novel, data-driven hypotheses in microbiome research.