Intermittent Hypoxia and Hypercapnia Reproducibly Change the Gut Microbiome and Metabolome across Rodent Model...

Area of Science:

• Microbiome research and metabolomics
• Animal models of disease
• Cardiovascular disease and sleep apnea modeling

Background:

• Gut microbiome perturbations in disease are studied using animal models.
• Translating findings across different animal models and to human populations is challenging.
• Limited studies compare interventions across multiple animal models using multi-omics data over time.

Purpose of the Study:

• To investigate if microbiome and metabolome changes are consistent across two mouse models of atherosclerosis under intermittent hypoxia and hypercapnia (IHH).
• To utilize time series multi-omics data and machine learning to relate different animal models.
• To identify robust microbial and metabolic biomarkers associated with IHH exposure.

Main Methods:

• Employed 16S rRNA amplicon profiling for microbiome analysis and untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) for metabolome profiling.
• Utilized two mouse models: ApoE-/- (n=24) and Ldlr-/- (n=16), exposed to IHH longitudinally for 10 and 6 weeks, respectively.
• Applied random forest classifiers to predict IHH exposure and cross-apply predictive features between models.

Main Results:

• Achieved excellent accuracy in predicting IHH exposure within each mouse model and when cross-applying predictive features.
• Identified key microbes (e.g., Mogibacteriaceae, Clostridiaceae) and metabolites (bile acids, fatty acids) that reproducibly predicted IHH exposure.
• Demonstrated the utility of time series multi-omics data for relating different animal models via supervised machine learning.

Conclusions:

• Time series multi-omics data combined with machine learning can effectively relate different animal models of disease.
• Identified a refined set of reproducible microbiome and metabolome biomarkers associated with IHH.
• Provides a pathway for identifying robust features that underpin translation from animal models to human disease, enhancing reproducibility in microbiome research.