Validation of a urine metabolome fingerprint in dog for phenotypic classification

Abstract

Selective breeding of dogs over hundreds of years has inadvertently resulted in breed-specific propensities to particular diseases. Furthermore, it has likely induced more subtle affects on the physiology of certain breeds and moved them from their evolutionary optima. In the absence of obvious disease phenotypes such subtle changes could have yet unrecognised breed-specific implications for health and well-being. Here we have applied NMR metabolomics as a discovery-driven approach to identify the impact of breed on the urinary profile of dog and to determine if non-disease-related breed differences can be identified. Multiple urines were collected non-invasively over a two-week period from seven neutered male Labrador retrievers and miniature Schnauzers. Following NMR analyses by 1-dimensional ¹H and 2-dimensional ¹H J-resolved (JRES) spectroscopy, principal component analysis revealed that the metabolic variability within each individual is relatively small compared to inter-individual variability, and that some separation between breeds was evident. A supervised model, using partial least squares discriminant analysis (PLS-DA) with class based upon breed, was trained using the JRES data. The model predicted correctly the breed of seven additional urines, yielding a model sensitivity and specificity of 100%. Several significant metabolic differences between the breeds were identified. A second model was developed using PLS-DA with class based upon individual dogs, which again achieved high classification accuracy for the test set. Overall, this confirms that canine urine is information-rich and that breed is a major determinant of urinary metabolic fingerprints. In the future this may enable a more accurate development of specific nutritional care for an individual or breed.