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

Abstract

INTRODUCTION: Ecological resilience is the capacity of an ecosystem to maintain its state and recover from disturbances. This concept can be applied to the gut microbiome as a marker of health. METHODS: Several metrics have been proposed to quantify microbiome resilience, based on the prior choice of some salient feature of the trajectories of microbiome change. We propose a data-driven approach based on compositional and functional data analysis to quantify microbiome resilience. We demonstrate the validity of our approach through applications to sled dogs undergoing three types of exercise: running on an exercise wheel, pulling an all-terrain vehicle, and pulling a sled. RESULTS: Microbiota composition was clearly impacted by each exercise type. Log-ratio analysis was utilized for dimensionality reduction and identified 33 variables (taxa) explaining 90% of the variance. Functional principal component analysis identified two scores (FPCA 1 and FPCA2) which explained 76% and 19% of the variability of the trajectories, respectively. More resilient trajectories corresponded to low values of FPCA1 and FPCA2 values close to zero. Levels of chemokines MCP-1 and KC-like, which increased significantly after exercise and returned to pre-exercise levels within 24 h, were significantly associated with FPCA scores as well. DISCUSSION: To our knowledge, this is the first study proposing a principled approach to quantify microbiome resilience in healthy dogs and associate it with immune response to exercise-related stress.