A bi-variate framework to model microbiome resilience in healthy dogs

Area of Science:

• Microbiome research
• Ecological resilience
• Animal physiology

Background:

• Ecological resilience describes an ecosystem's ability to recover from disturbances.
• This concept is applicable to the gut microbiome as a health indicator.
• Existing metrics for microbiome resilience rely on pre-selected features of microbial community changes.

Purpose of the Study:

• To propose and validate a data-driven approach for quantifying gut microbiome resilience.
• To apply this method to sled dogs experiencing different exercise types.
• To associate microbiome resilience with physiological responses to exercise stress.

Main Methods:

• Utilized compositional and functional data analysis for microbiome resilience quantification.
• Employed log-ratio analysis for dimensionality reduction, identifying key taxa.
• Applied functional principal component analysis (FPCA) to analyze trajectory variability.

Main Results:

• Exercise type significantly impacted microbiota composition.
• FPCA identified two main scores explaining substantial variability in microbiome trajectories.
• Resilient trajectories were associated with low FPCA1 and near-zero FPCA2 values.
• Chemokine levels (MCP-1, KC-like) correlated with FPCA scores, indicating immune response linkage.

Conclusions:

• This study introduces a principled, data-driven method to quantify canine gut microbiome resilience.
• The findings associate exercise-induced microbiome shifts with specific immune markers.
• This research provides novel insights into the gut microbiome's role in exercise adaptation and health in dogs.