Quantitative Mass Spectrometry Normalization in Urine Biomarker Analysis in Nephrotic Syndrome

Glomerular Dis. 2022 Jul;2(3):121-131. doi: 10.1159/000522217. Epub 2022 Jan 27.

Abstract

Chronic kidney disease (CKD) affects 30 million adults, costs ~$79 billion dollars (2016) in Medicare expenditures, and is the ninth leading cause of death in the United States. The disease is silent or undiagnosed in almost half of people with severely reduced kidney function. Urine provides an ideal biofluid that is accessible to high-sensitivity mass spectrometry-based proteomic interrogation and is an indicator of renal homeostasis. While the accurate and precise diagnosis and better disease management of CKD can be aided using urine biomarkers, their discovery in excessive protein or nephrotic urine samples can present challenges. In this work we present a mass spectrometry-based method utilizing multiplex tandem mass tag (TMT) quantification and improved protein quantification using reporter ion normalization to urinary creatinine to analyze urinary proteins from patients with a form of nephrotic syndrome (FSGS). A comparative analysis was performed for urine from patients in remission versus active disease flare. Two-dimensional LC-MS/MS TMT quantitative analysis identified over 1058 urine proteins, 580 proteins with 2 peptides or greater and quantifiable. Normalization of TMT abundance values to creatinine per ml of urine concentrated reduced variability in 2D-TMT-LC-MS/MS experiments. Univariate and multivariate analyses showed that 27 proteins were significantly increased in proteinuric disease flare. Hierarchical heatmap clustering showed that SERPINA1 and ORM1 were >1.5 fold increased in active disease versus remission urine samples. ELISA validation of SERPINA1 and ORM1 abundance agreed with our quantitative TMT proteomics analysis. These findings provide support for the utility of this method for identification of novel diagnostic markers of CKD and identify SERPINA1 and ORM1 as promising candidate diagnostic markers for FSGS.

Keywords: biomarker; kidney disease; nephrotic syndrome; proteomics; tandem mass tag.