URINARY PROTEIN PROFILING FOR POTENTIAL BIOMARKERS OF CHRONIC KIDNEY DISEASE: A PILOT STUDY
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Date
2022
Authors
Gaipov, Abduzhappar
Makhammajanov, Zhalaliddin
Dauyey, Zhanna
Markhametova, Zhannur
Mussina, Kamilla
Nogaibayeva, Assem
Kozina, Larissa
Auganova, Dana
Tarlykov, Pavel
Bukasov, Rostislav
Journal Title
Journal ISSN
Volume Title
Publisher
Diagnostics
Abstract
Proteinuria is a risk factor for chronic kidney disease (CKD) progression and associated complications. However, there is insufficient information on individual protein components in urine and the severity of CKD. We aimed to investigate urinary proteomics and its association with proteinuria and kidney function in early-stage CKD and in healthy individuals. A 24 h urine sample of 42 individuals (21-CKD and 21-healthy individuals) was used for mass spectrometry-based proteomics analysis. An exponentially modified protein abundance index (emPAI) was calculated for each protein. Data were analyzed by Mascot software using the SwissProt database and bioinformatics tools. Overall, 298 unique proteins were identified in the cohort; of them, 250 proteins belong to the control group with median (IQR) emPAI 39.1 (19–53) and 142 proteins belong to the CKD group with median (IQR) emPAI 67.8 (49–117). The level of 24 h proteinuria positively correlated with emPAI (r = 0.390, p = 0.011). The emPAI of some urinary proteomics had close positive (ALBU, ZA2G, IGKC) and negative (OSTP, CD59, UROM, KNG1, RNAS1, CD44, AMBP) correlations (r < 0.419, p < 0.001) with 24 h proteinuria levels. Additionally, a few proteins (VTDB, AACT, A1AG2, VTNC, and CD44) significantly correlated with kidney function. In this proteomics study, several urinary proteins correlated with proteinuria and kidney function. Pathway analysis identified subpathways potentially related to early proteinuric CKD, allowing the design of prospective studies that explore their response to therapy and their relationship to long-term outcomes.
Description
Keywords
urinary proteomics, proteinuria, chronic kidney disease, biomarkers
Citation
Gaipov, A., Makhammajanov, Z., Dauyey, Z., Markhametova, Z., Mussina, K., Nogaibayeva, A., Kozina, L., Auganova, D., Tarlykov, P., Bukasov, R., Utegulov, Z., Turebekov, D., Soler, M. J., Ortiz, A., & Kanbay, M. (2022). Urinary Protein Profiling for Potential Biomarkers of Chronic Kidney Disease: A Pilot Study. Diagnostics, 12(11), 2583. https://doi.org/10.3390/diagnostics12112583