Comprehensive urinary metabolomic characterization of a genetically induced mouse model of prostatic inflammation

Dysfunction of the lower urinary tract commonly afflicts the middle-aged and aging male population. The etiology of lower urinary tract symptoms (LUTS) is multifactorial. Benign prostate hyperplasia, fibrosis, smooth muscle contractility, and inflammation likely contribute. Here we aim to characterize the urinary metabolomic profile associated with prostatic inflammation, which could inform future personalized diagnosis or treatment, as well as mechanistic research. Quantitative urinary metabolomics was conducted to examine molecular changes following induction of inflammation via conditional Interleukin-1β expression in prostate epithelia using a novel transgenic mouse strain. To advance method development for urinary metabolomics, we also compared different urine normalization methods and found that normalizing urine samples based on osmolality prior to LC-MS most completely separated urinary metabolite profiles of mice with and without prostate inflammation via principal component analysis. Global metabolomics was combined with advanced machine learning feature selection and classification for data analysis. Key dysregulated metabolites and pathways were identified and were relevant to prostatic inflammation, some of which overlapped with our previous study of human LUTS patients. A binary classification model was established via the support vector machine algorithm to accurately differentiate control and inflammation groups, with an area-under-the-curve value of the receiver operating characteristic of 0.81, sensitivity of 0.974 and specificity of 0.995, respectively. This study generated molecular profiles of non-bacterial prostatic inflammation, which could assist future efforts to stratify LUTS patients and develop new therapies.