Urine Phenotyping Core

Core director:
John C. Lieske, M.D.

Nephrolithiasis is common, affecting up to 10 percent of the adult U.S. population. Symptomatic stone episodes produce significant pain and suffering, as well as great individual and societal economic costs.

Much effort has been directed toward understanding metabolic abnormalities that can increase urinary supersaturation, which is clearly one key factor that initiates and favors stone growth. However, crystal-crystal and crystal-cell interactions also appear to be critical events during the early stages of stone formation.

Therefore, increased understanding of the factors that modulate the interface of urinary proteins and crystals, and hence their subsequent interaction with other crystals or cells, is a necessary prerequisite for identifying new therapeutic targets. Such knowledge will in turn enable development of new strategies for the treatment and prevention of renal stone disease.

Therefore, the Urine Phenotyping Core serves four key functions in support of the O'Brien Urology Research Center. The core's functions are:

  • Quantification of urinary components of the supersaturation profile
  • Quantification of urinary inhibitor activity
  • Quantification of known urinary macromolecular inhibitors
  • Application of differential proteomics to urine samples of stone forming and control populations

The Urine Phenotyping Core directly supports the CT and Urinary Correlates of Renal Stone Precursor Lesions Project, the Epidemiology of Nephrolithiasis and Chronic Kidney Disease Project, and the Pilot and Feasibility Studies Program, although it may also be of value for future pilot projects.

Given the explosion of proteomic techniques and their ready applications to urine, the Urine Phenotyping Core is ideally situated to provide the O'Brien Urology Research Center researchers ready access to a modern proteomics tool kit, taking advantage of resources and expertise of Mayo's Proteomics Core. This includes access to the latest mass spectrometer technology available, as well as bioinformatics resources necessary to analyze and interpret the large data sets typically produced in modern proteomics experiments.