Dogs have a key role in law enforcement and military work, and research with the goal of improving working dog performance is ongoing. While there have been intriguing studies from lab animal models showing a potential connection between the gut microbiome and behavior or mental health there is a dearth of studies investigating the microbiome-behavior relationship in working dogs. The overall objective of this study was to characterize the microbiota of working dogs and to determine if the composition of the microbiota is associated with behavioral and performance outcomes. Freshly passed stools from each working canine (Total n = 134) were collected and subject to shotgun metagenomic sequencing using Illumina technology. Behavior, performance, and demographic metadata were collected. Descriptive statistics and prediction models of behavioral/phenotypic outcomes using gradient boosting classification based on Xgboost were used to study associations between the microbiome and outcomes. Regarding machine learning methodology, only microbiome features were used for training and predictors were estimated in cross-validation. Microbiome markers were statistically associated with motivation, aggression, cowardice/hesitation, sociability, obedience to one trainer vs many, and body condition score (BCS). When prediction models were developed based on machine learning, moderate predictive power was observed for motivation, sociability, and gastrointestinal issues. Findings from this study suggest potential gut microbiome markers of performance and could potentially advance care for working canines.
ASJC Scopus subject areas
- Applied Microbiology and Biotechnology