Candidate protein biomarkers in chronic kidney disease: a proteomics study.
Biomarkers
Chronic kidney disease
Proteinuria
Urinary proteomics
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 Jun 2024
18 Jun 2024
Historique:
received:
23
03
2024
accepted:
13
06
2024
medline:
19
6
2024
pubmed:
19
6
2024
entrez:
18
6
2024
Statut:
epublish
Résumé
Proteinuria poses a substantial risk for the progression of chronic kidney disease (CKD) and its related complications. Kidneys excrete hundreds of individual proteins, some with a potential impact on CKD progression or as a marker of the disease. However, the available data on specific urinary proteins and their relationship with CKD severity remain limited. Therefore, we aimed to investigate the urinary proteome and its association with kidney function in CKD patients and healthy controls. The proteomic analysis of urine samples showed CKD stage-specific differences in the number of detected proteins and the exponentially modified protein abundance index for total protein (p = 0.007). Notably, specific urinary proteins such as B2MG, FETUA, VTDB, and AMBP exhibited robust negative associations with kidney function in CKD patients compared to controls. Also, A1AG2, CD44, CD59, CERU, KNG1, LV39, OSTP, RNAS1, SH3L3, and UROM proteins showed positive associations with kidney function in the entire cohort, while LV39, A1BG, and CERU consistently displayed positive associations in patients compared to controls. This study suggests that specific urinary proteins, which were found to be negatively or positively associated with the kidney function of CKD patients, can serve as markers of dysfunctional or functional kidneys, respectively.
Identifiants
pubmed: 38890379
doi: 10.1038/s41598-024-64833-8
pii: 10.1038/s41598-024-64833-8
doi:
Substances chimiques
Biomarkers
0
Proteome
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
14014Subventions
Organisme : Nazarbayev University Collaborative Research Program
ID : 211123CRP1603
Informations de copyright
© 2024. The Author(s).
Références
Sundström, J. et al. Prevalence, outcomes, and cost of chronic kidney disease in a contemporary population of 2.4 million patients from 11 countries: The CaReMe CKD study. Lancet Reg. Health Eur. 20, 100438 (2022).
pubmed: 36090671
pmcid: 9459126
doi: 10.1016/j.lanepe.2022.100438
Hill, N. R. et al. Global prevalence of chronic kidney disease—a systematic review and meta-analysis. PLoS ONE 11, e0158765 (2016).
pubmed: 27383068
pmcid: 4934905
doi: 10.1371/journal.pone.0158765
Wang, H. et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1459–1544 (2016).
doi: 10.1016/S0140-6736(16)31012-1
Thomas, B. et al. Global cardiovascular and renal outcomes of reduced GFR. J. Am. Soc. Nephrol. 28, 2167 (2017).
pubmed: 28408440
pmcid: 5491277
doi: 10.1681/ASN.2016050562
Foreman, K. J. et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: Reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet 392, 2052–2090 (2018).
pubmed: 30340847
pmcid: 6227505
doi: 10.1016/S0140-6736(18)31694-5
Gansevoort, R. T. et al. Chronic kidney disease and cardiovascular risk: Epidemiology, mechanisms, and prevention. Lancet 382, 339–352 (2013).
pubmed: 23727170
doi: 10.1016/S0140-6736(13)60595-4
Kalantar-Zadeh, K., Jafar, T. H., Nitsch, D., Neuen, B. L. & Perkovic, V. J. T. I. Chronic kidney disease. Lancet 398, 786–802 (2021).
pubmed: 34175022
doi: 10.1016/S0140-6736(21)00519-5
Cravedi, P. & Remuzzi, G. Pathophysiology of proteinuria and its value as an outcome measure in chronic kidney disease. Brit. J. Clin. Pharmacol. 76, 516–523 (2013).
doi: 10.1111/bcp.12104
Chen, C.-H. et al. Proteinuria as a therapeutic target in advanced chronic kidney disease: A retrospective multicenter cohort study. Sci. Rep. 6, 26539 (2016).
pubmed: 27198863
pmcid: 4873744
doi: 10.1038/srep26539
Krolewski, A. S., Skupien, J., Rossing, P. & Warram, J. H. Fast renal decline to end-stage renal disease: An unrecognized feature of nephropathy in diabetes. Kidney Int. 91, 1300–1311 (2017).
pubmed: 28366227
pmcid: 5429989
doi: 10.1016/j.kint.2016.10.046
Makhammajanov, Z. et al. Tubular toxicity of proteinuria and the progression of chronic kidney disease. Nephrol. Dial. Transpl. 39, 589–599 (2024).
doi: 10.1093/ndt/gfad215
Ramírez Medina, C. R. et al. Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry. Clin. Proteomics 20, 19 (2023).
pubmed: 37076799
pmcid: 10116780
doi: 10.1186/s12014-023-09405-0
Good, D. M. et al. Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol. Cell. Proteomics 9, 2424–2437 (2010).
pubmed: 20616184
pmcid: 2984241
doi: 10.1074/mcp.M110.001917
Choi, Y. W. et al. Potential urine proteomics biomarkers for primary nephrotic syndrome. Clin. Proteomics 14, 1–9 (2017).
doi: 10.1186/s12014-017-9153-1
Levin, A. et al. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. Suppl. 3, 1–150 (2013).
Inker, L. A. et al. New creatinine-and cystatin C-based equations to estimate GFR without race. N. Engl. J. Med. 385, 1737–1749 (2021).
pubmed: 34554658
pmcid: 8822996
doi: 10.1056/NEJMoa2102953
Gaipov, A. et al. Development and validation of hybrid Brillouin-Raman spectroscopy for non-contact assessment of mechano-chemical properties of urine proteins as biomarkers of kidney diseases. BMC Nephrol. 21, 1–9 (2020).
doi: 10.1186/s12882-020-01890-x
Sun, W., Gao, Y. J. R., Methods, U. P. Protocols. In Renal and Urinary Proteomics: Methods and Protocols. 271–279 (Wiley, 2009).
Ishihama, Y. et al. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein* s. Mol. Cell. Proteomics 4, 1265–1272 (2005).
pubmed: 15958392
doi: 10.1074/mcp.M500061-MCP200
org. Hs. eg. db: Genome wide annotation for Human v. R package version 3.16.0. (2022).
Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
pubmed: 34557778
pmcid: 8454663
Yu, G. & He, Q.-Y.J.M.B. ReactomePA: An R/Bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 12, 477–479 (2016).
pubmed: 26661513
doi: 10.1039/C5MB00663E
Szklarczyk, D. et al. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).
pubmed: 30476243
doi: 10.1093/nar/gky1131
Wickham, H. & Wickham, H. Data Analysis (Springer, 2016).
Shama, A. et al. The latest developments in using proteomic biomarkers from urine and serum for non-invasive disease diagnosis and prognosis. Biomark. Insights 18, 11772719231190218 (2023).
pubmed: 37528936
pmcid: 10387783
doi: 10.1177/11772719231190218
Borberg, E., Pashko, S., Koren, V., Burstein, L. & Patolsky, F. J. A. C. Depletion of highly abundant protein species from biosamples by the use of a branched silicon nanopillar on-chip platform. Anal. Chem. 93, 14527–14536 (2021).
pubmed: 34668374
pmcid: 8592501
doi: 10.1021/acs.analchem.1c03506
Filip, S. et al. Comparison of depletion strategies for the enrichment of low-abundance proteins in urine. PLoS ONE 10, e0133773 (2015).
pubmed: 26208298
pmcid: 4514849
doi: 10.1371/journal.pone.0133773
Govender, I. S., Mokoena, R., Stoychev, S. & Naicker, P. J. P. Urine-HILIC: Automated sample preparation for bottom-up urinary proteome profiling in clinical proteomics. Proteomes 11, 29 (2023).
pubmed: 37873871
pmcid: 10594433
doi: 10.3390/proteomes11040029
Kalantari, S. et al. Urinary prognostic biomarkers and classification of IgA nephropathy by high resolution mass spectrometry coupled with liquid chromatography. PLoS ONE 8, e80830 (2013).
pubmed: 24339887
pmcid: 3855054
doi: 10.1371/journal.pone.0080830
Prikryl, P. et al. Proteomic approach for identification of IgA nephropathy-related biomarkers in urine. Physiol. Res. 66, 621–632 (2017).
pubmed: 28406696
doi: 10.33549/physiolres.933380
Schaeffer, C., Devuyst, O. & Rampoldi, L. Uromodulin: Roles in health and disease. Annu. Rev. Physiol. 83, 477–501 (2021).
pubmed: 33566673
doi: 10.1146/annurev-physiol-031620-092817
Shoukry, A., Bdeer, S.E.-A. & El-Sokkary, R. H. J. M. Urinary monocyte chemoattractant protein-1 and vitamin D-binding protein as biomarkers for early detection of diabetic nephropathy in type 2 diabetes mellitus. Mol. Cell Biochem. 408, 25–35 (2015).
pubmed: 26104579
doi: 10.1007/s11010-015-2479-y
Argyropoulos, C. P. et al. Rediscovering beta-2 microglobulin as a biomarker across the spectrum of kidney diseases. Front. Med. 4, 73 (2017).
doi: 10.3389/fmed.2017.00073
Bassey, P. E. et al. Causal association pathways between fetuin-A and kidney function: A mediation analysis. J. Int. Med. Res. 50, 03000605221082874 (2022).
pubmed: 35435033
pmcid: 9019358
doi: 10.1177/03000605221082874
Gaipov, A. et al. Urinary protein profiling for potential biomarkers of chronic kidney disease: A pilot study. Diagnostics 12, 2583 (2022).
pubmed: 36359427
pmcid: 9689510
doi: 10.3390/diagnostics12112583
Christensen, E. I., Birn, H., Storm, T., Weyer, K. & Nielsen, R. J. P. Endocytic receptors in the renal proximal tubule. Physiology 27, 223–236 (2012).
pubmed: 22875453
doi: 10.1152/physiol.00022.2012
Jaswanth, C. et al. Short-term changes in urine beta 2 microglobulin following recovery of acute kidney injury resulting from snake envenomation. Kidney Int. Rep. 4, 667–673 (2019).
pubmed: 31080921
pmcid: 6506712
doi: 10.1016/j.ekir.2019.01.016
Puthiyottil, D. et al. Role of urinary beta 2 microglobulin and kidney injury molecule-1 in predicting kidney function at one year following acute kidney injury. Int. J. Nephrol. Renov. 14, 225–234 (2021).
doi: 10.2147/IJNRD.S319933
Fels, J. et al. Cadmium complexed with β2-microglubulin, albumin and lipocalin-2 rather than metallothionein cause megalin: Cubilin dependent toxicity of the renal proximal tubule. Int. J. Mol. Sci. 20, 2379 (2019).
pubmed: 31091675
pmcid: 6566203
doi: 10.3390/ijms20102379
Hao, Y. et al. Changes of protein levels in human urine reflect the dysregulation of signaling pathways of chronic kidney disease and its complications. Sci. Rep. 10, 20743 (2020).
pubmed: 33247215
pmcid: 7699629
doi: 10.1038/s41598-020-77916-z
Piazzon, N. et al. Urine Fetuin-A is a biomarker of autosomal dominant polycystic kidney disease progression. J. Transl. Med. 13, 1–11 (2015).
doi: 10.1186/s12967-015-0463-7
Chekol Abebe, E. et al. The structure, biosynthesis, and biological roles of fetuin-A: A review. Front. Cell. Dev. Biol. 10, 945287 (2022).
pubmed: 35923855
pmcid: 9340150
doi: 10.3389/fcell.2022.945287
Kristiansson, A. et al. α1-Microglobulin (A1M) protects human proximal tubule epithelial cells from heme-induced damage in vitro. Int. J. Mol. Sci. 21, 5825 (2020).
pubmed: 32823731
pmcid: 7461577
doi: 10.3390/ijms21165825
Amatruda, J. G. et al. Biomarkers of kidney tubule disease and risk of end-stage kidney disease in persons with diabetes and CKD. Kidney Int. Rep. 7, 1514–1523 (2022).
pubmed: 35812302
pmcid: 9263389
doi: 10.1016/j.ekir.2022.03.033
Lopez, M. J., Royer, A. & Shah, N. J. Biochemistry, Ceruloplasmin (StatPearls Publishing, 2020).
Ito, S. et al. Urinary copper excretion in type 2 diabetic patients with nephropathy. Nephron 88, 307–312 (2001).
pubmed: 11474224
doi: 10.1159/000046013
Piyaphanee, N. et al. Discovery and initial validation of α 1-B glycoprotein fragmentation as a differential urinary biomarker in pediatric steroid-resistant nephrotic syndrome. Proteomics Clin. Appl. 5, 334–342 (2011).
pubmed: 21591266
pmcid: 7039306
doi: 10.1002/prca.201000110
Brasileiro-Martins, L. M. et al. Urinary proteomics reveals biological processes related to acute kidney injury in Bothrops atrox envenomings. PLoS Negl. Trop. Dis. 18, e0012072 (2024).
pubmed: 38536893
pmcid: 11020875
doi: 10.1371/journal.pntd.0012072
Van Nynatten, L. R. et al. A novel multiplex biomarker panel for profiling human acute and chronic kidney disease. Sci. Rep. 13, 21210 (2023).
pubmed: 38040779
pmcid: 10692319
doi: 10.1038/s41598-023-47418-9
Stephan, J.-P. et al. Albumin stimulates the accumulation of extracellular matrix in renal tubular epithelial cells. Am. J. Nephrol. 24, 14–19 (2004).
pubmed: 14654729
doi: 10.1159/000075347
Gros, A., Ollivier, V. & Ho-Tin-Noé, B. Platelets in inflammation: Regulation of leukocyte activities and vascular repair. Front. Immunol. 5, 678 (2015).
pubmed: 25610439
pmcid: 4285099
doi: 10.3389/fimmu.2014.00678
Finsterbusch, M., Schrottmaier, W. C., Kral-Pointner, J. B., Salzmann, M. & Assinger, A. J. P. Measuring and interpreting platelet-leukocyte aggregates. Platelets 29, 677–685 (2018).
pubmed: 29461910
pmcid: 6178087
doi: 10.1080/09537104.2018.1430358