Urinary Protein And Peptide Markers in Chronic Kidney Disease Part 3

8. Non-Specific Urinary Protein Markers 

Uromodulin, collagens, A1AT, and their fragments are the main non-specific urine protein markers that were identified in all the aforementioned nephropathies (Table 2), as well as in many other disorders associated with renal dysfunction or proteinuria [17–39]. Uromodulin is a kidney-specific glycosylphosphatidylinositol (GPI)-anchored glycoprotein exclusively produced by the epithelial cells lining the thick ascending limb of the loop of Henle and is a normal component of the urine. Collagen peptides are also normally present in urine and reflect the turnover of the extracellular matrix in kidney tissues. Nevertheless, both usual urine components may indicate pathological changes. Uromodulin may also be a potential biomarker relevant to tubular function and CKD [113]. The level of collagen fragments strongly correlates with the initiation of DN [13,17,19,45,72]; quantitative changes in these fragments in urine were noted 3-5 years prior to the development of macroalbuminuria [19]. Overall, the qualitative composition of the collagen fragments can vary in different nephropathies [45,47,54,72].

Unlike uromodulin and collagen peptides, the appearance of A1AT in urine is always associated with some type of pathology and may reflect podocyte stress [53]. Notably, an increase in urinary A1AT was observed in all the nephropathies reviewed in the present study (Table 2).

In general, the assessment of non-specific markers in combination with specific markers significantly improved the differentiation of nephropathies. In particular, the levels of six UMOD and A1AT peptides differentiated between proliferative and nonproliferative (including MCD, MN, FSGS, and IgAN) forms of glomerular kidney diseases [58]. Moreover, uromodulin overexpression was shown to predispose one to CKDs such as hypertensive nephropathy and DN [114]. The detection of collagen fragments together with the LG3 fragment of endorepellin is crucial for diagnosing IgAN, as collagen may indicate a more severe disease course with impaired angiogenesis and the rapid development of kidney fibrosis [64]. Estimating the levels of A1AT, uromodulin, transferrin, serum albumin, and α-1-β-glycoprotein is also important in IgAN, as such levels reflect common pathological processes, including enhanced apoptosis, inflflammation, coagulation, and complement activation [45,54,61,62,64,65,72]. 

9. Conclusions

In recent years, research into the use of stem cells and a Chinese herbal remedy for the treatment of kidney diseases has gained great attention. The main mechanism of the two therapies is to promote the repair of injured renal tissues and protect the remaining renal functions. 

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The Chinese herbal remedy,cistanche, has been used in traditional Chinese medicine to treat various chronic kidney diseases since ancient times. It is reported that cistanche has the potential to reduce inflammation, reduce kidney fibrosis, and promote the synthesis of extracellular matrix components. It has been revealed that these effects are due to its bioactive components, including many phenolic substances, triterpenoids, and coumarins.

On the other hand, stem cell technology has caused a revolution in medical practice. Research has demonstrated that stem cells can differentiate into various types of renal cells and perform therapeutic activities, including protecting the remaining functional renal tissues, slowing down tissue fibrosis, and repairing damaged renal tissues.

Ultimately, the combination of traditional Chinese medicine with modern science could be the key to treating various kidney diseases. This strategy has gradually been accepted by the medical community and studies have already shown that the combined therapy of cistanche and stem cell treatment may considerably reduce the mortality rate of kidney diseases. 

In conclusion, the use of cistanche and stem cell treatment in the treatment of kidney diseases shows great potential and requires further research. The combined therapy of the two treatments could provide an improved treatment option for those facing kidney diseases.

The main feature of the proteomic analysis is that many of the markers detected in urine are observed as the result of protein penetration from the blood (albumin, retinol-binding protein, etc.) or as reflections of common pathological processes such as extracellular matrix accumulation (collagens and A1AT), the deposition of immunoglobulin complexes, complement activation, apoptosis, lipid oxidation, and tubular dysfunction (β-2-microglobulin, uromodulin, etc.) with high proteinuria. In this case, it is crucial to assess quantitative changes in these indicators to accurately reflect the processing activity and damage severity.

One of the most important goals of urine proteomic analysis in patients with CKD is determining disease-specific biomarkers or their combinations. Proteins extracted for the first time warrant the most attention, as they may reflect the most important pathogenetic stages in disease development. For example, CD44, a marker of activated parietal epithelial cells, may reflect the processes of glomerulosclerosis in MN [50] or IgAN [38] but, at the same time, may also be an essential feature for differentiating FSGS from MCD [52]. DPEP1, primarily identified in FSGS, is thought to reflect TRPC6 activation in podocytes [52]; ubiquitin-60S ribosomal protein L40 (UBA52), which is a marker of cellular stress; or components of the podocyte cytoskeleton that are damaged by antibodies [49,115]. Apolipoproteins, which can play a potential role in FSGS pathogenesis as “permeability factors” [116], as well as proteins whose roles are not yet completely understood, such as lysosome membrane protein-2 and afamin in MN [56,57] and the laminin G-like 3 (LG3) fragment of endorepellin in IgAN [64], may reflect pathological processes and could become targets for new approaches to immunosuppressive or nephroprotective therapy. In addition, positive dynamic changes in the proteomic profile after the designated therapy may help to confirm whether the prescribed medications were chosen correctly and are helping to achieve the desired outcomes. However, despite the validation of the CKD 273 classifier in several studies, there is a need to further develop new panels with increased specificity for specific nephropathies. This seems to be the most important goal for further proteomics research. 

Author Contributions

Conceptualization, N.C. and A.S.K.; writing—original draft preparation, N.C., A.V., V.M., A.S.K.; writing—review and editing, N.V.Z., M.I.I.; supervision, S.M., E.N.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Russian Science Foundation, grant #21-74-20173. 

Institutional Review Board Statement 

Not applicable. 

Informed Consent Statement

Not applicable. 

Data Availability Statement

Not applicable.

Acknowledgments

We extend our appreciation to anonymous reviewers whose valuable comments substantially improved the manuscript. 

Conflicts of Interest

The authors declare no conflicts of interest. 

References 

4. Alani, H.; Tamimi, A.; Tamimi, N. Cardiovascular co-morbidity in chronic kidney disease: Current knowledge and future research needs. World J. Nephrol. 2014, 3, 156–168. 

5. Hsu, C.; Ordoñez, J.; Chertow, G.; Fan, D.; McCulloch, C.; Go, A. The risk of acute renal failure in patients with chronic kidney disease. Kidney Int. 2008, 74, 101–107.

6. Tonelli, M.; Wiebe, N.; Culleton, B.; House, A.; Rabbat, C.; Fok, M.; McAlister, F.; Garg, A.X. Chronic Kidney Disease and Mortality Risk: A Systematic Review. J. Am. Soc. Nephrol. 2006, 17, 2034–2047. 

7. Hsu, C.-Y.; Iribarren, C.; McCulloch, C.E.; Darbinian, J.; Go, A.S. Risk Factors for End-Stage Renal Disease: 25-year follow-up. Arch. Intern. Med. 2009, 169, 342–350.  8. Hill, N.R.; Fatoba, S.T.; Oke, J.L.; Hirst, J.; O’Callaghan, C.A.; Lasserson, D.; Hobbs, R. Global Prevalence of Chronic Kidney Disease—A Systematic Review and Meta-Analysis. PLoS ONE 2016, 11, e0158765.

9. Schieppati, A.; Remuzzi, G. Chronic renal diseases as a public health problem: Epidemiology, social, and economic implications. Kidney Int. 2005, 68, S7–S10. 

10. Bommer, J. Prevalence and socio-economic aspects of chronic kidney disease. Nephrol. Dial. Transplant. 2002, 17, 8–12.

11. Vos, T.; Allen, C.; Arora, M.; Barber, R.M.; Bhutta, Z.A.; Brown, A.; Carter, A.; Casey, D.C.; Charlson, F.J.; Chen, A.Z.; et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet 2015, 388, 1545–1602. 

12. Dhaun, N.; Bellamy, C.O.; Cattran, D.C.; Kluth, D.C. Utility of renal biopsy in the clinical management of the renal disease. Kidney Int. 2014, 85, 1039–1048. 

13. Filip, S.; Pontillo, C.; Schanstra, J.P.; Vlahou, A.; Mischak, H.; Klein, J. Urinary proteomics and molecular determinants of chronic kidney disease: Possible link to proteases. Expert Rev. Proteom. 2014, 11, 535–548. 

14. Mischak, H.; Delles, C.; Vlahou, A.; Vanholder, R. Proteomic biomarkers in kidney disease: Issues in development and implementation. Nat. Rev. Nephrol. 2015, 11, 221–232. 

15. Decramer, S.; Gonzalez de Peredo, A.; Breuil, B.; Mischak, H.; Monsarrat, B.; Bascands, J.-L.; Schanstra, J.P. Urine in Clinical Proteomics. Mol. Cell. Proteom. 2008, 7, 850–1862.  16. Thomas, S.; Hao, L.; Ricke, W.; Li, L. Biomarker discovery in mass spectrometry-based urinary proteomics. Proteom. Clin. Appl. 2016, 10, 358–370. 

17. Argiles, A.; Siwy, J.; Duranton, F.; Gayrard, N.; Dakna, M.; Lundin, U.; Osaba, L.; Delles, C.; Mourad, G.; Weinberger, K.M.; et al. CKD273, a New Proteomics Classifier Assessing CKD and Its Prognosis. PLoS ONE 2013, 8, e62837. 

18. Schanstra, J.P.; Zürbig, P.; Alkhalaf, A.; Argiles, A.; Bakker, S.J.L.; Beige, J.; Bilo, H.J.G.; Chatzikyroku, C.; Dakna, M.; Dawson, J.; et al. Diagnosis and prediction of CKD progression by assessment of urinary peptides. JASN 2015, 26, 1999–2010. 

19. Zürbig, P.; Jerums, G.; Hovind, P.; MacIsaac, R.J.; Mischak, H.; Nielsen, S.E.; Panagiotopoulos, S.; Persson, F.; Rossing, P. Urinary Proteomics for Early Diagnosis in Diabetic Nephropathy. Diabetes 2012, 61, 3304–3313. 

20. Celis, J.E.; Gromova, I.; Moreira, J.M.; Cabezon, T.; Gromov, P. Impact of proteomics on bladder cancer research. Pharmacogenomics 2004, 5, 381–394.

21. Chen, Y.-T.; Chen, H.-W.; Domanski, D.; Smith, D.S.; Liang, K.-H.; Wu, C.-C.; Chen, C.-L.; Chung, T.; Chen, M.-C.; Chang, Y.-S.; et al. Multiplexed quantification of 63 proteins in human urine by multiple reactions monitoring-based mass spectrometry for the discovery of potential bladder cancer biomarkers. J. Proteom. 2012, 75, 3529–3545.

22. Shi, T.; Gao, Y.; Quek, S.I.; Fillmore, T.L.; Nicora, C.D.; Su, D.; Zhao, R.; Kagan, J.L.; Srivastava, S.; Rodland, K.D.; et al. A Highly Sensitive Targeted Mass Spectrometric As-say for Quantification of AGR2 Protein in Human Urine and Serum. J. Proteom. Res. 2014, 2, 875–882. 

23. Ye, B.; Skates, S.; Mok, S.C.; Horick, N.K.; Rosenberg, H.F.; Vitonis, A.; Edwards, D.; Sluss, P.; Han, W.K.; Berkowitz, R.S.; et al. Proteomic-Based Discovery and Characterization of Glycosylated Eosinophil-Derived Neurotoxin and COOH-Terminal Osteopontin Fragments for Ovarian Cancer in Urine. Clin. Cancer Res. 2006, 12, 432–441. 

24. Mischak, H.; Kaiser, T.; Walden, M.; Hillmann, M.; Wittke, S.; Herrmann, A.; Knueppel, S.; Haller, H.; Fliser, D. Proteomic analysis for the assessment of diabetic renal damage in humans. Clin. Sci. 2004, 107, 485–495.

25. Buhimschi, I.A.; Zhao, G.; Funai, E.F.; Harris, N.; Sasson, I.E.; Bernstein, I.M.; Saade, G.R.; Buhimschi, C.S. Proteomic profiling of urine identifies specific fragments of SERPINA1 and albumin as biomarkers of preeclampsia. Am. J. Obstet. Gynecol. 2008, 199, 551.e1–551.e16.

26. Carty, D.M.; Siwy, J.; Brennand, J.E.; Zürbig, P.; Mullen, W.; Franke, J.; McCulloch, J.W.; North, R.A.; Chappell, L.C.; Mischak, H.; et al. Urinary Proteomics for Prediction of Preeclampsia. Hypertension 2011, 57, 561–569. 

27. Kononikhin, A.S.; Zakharova, N.V.; Sergeeva, V.A.; Indeykina, M.I.; Starodubtseva, N.L.; Bugrova, A.E.; Muminova, K.T.; Khodzhaeva, Z.S.; Popov, I.A.; Shao, W.; et al. Differential Diagnosis of Preeclampsia Based on Urine Peptidome Features Revealed by High Resolution Mass Spectrometry. Diagnostics 2020, 10, 1039. 

28. Ward, D.G.; Nyangoma, S.; Joy, H.; Hamilton, E.; Wei, W.; Tselepis, C.; Steven, N.; Wakelam, M.J.; Johnson, P.J.; Ismail, T.; et al. Proteomic profiling of urine for the detection of colon cancer. Proteom. Sci. 2008, 6, 19.

29. Tantipaiboonwong, P.; Sinchaikul, S.; Sriyam, S.; Phutrakul, S.; Chen, S.-T. Different techniques for urinary protein analysis of normal and lung cancer patients. Proteomics 2005, 5, 1140–1149.

30. Metzger, J.; Negm, A.A.; Plentz, R.R.; Weismüller, T.J.; Wedemeyer, J.; Karlsen, T.H.; Dakna, M.; Mullen, W.; Mischak, H.; Manns, M.P.; et al. Urine proteomic analysis differentiates cholangiocarcinoma from primary sclerosing cholangitis and other benign biliary disorders. Gut 2012, 62, 122–130.

31. Zimmerli, L.U.; Schiffer, E.; Zürbig, P.; Good, D.M.; Kellmann, M.; Mouls, L.; Pitt, A.R.; Coon, J.J.; Schmeider, R.D.; Peter, K.H.; et al. Urinary Proteomic Biomarkers in Coronary Artery Disease. Mol. Cell. Proteom. 2008, 7, 290–298. 

32. Kaiser, T.; Kamal, H.; Rank, A.; Kolb, H.-J.; Holler, E.; Ganser, A.; Hertenstein, B.; Mischak, B.; Weisseinger, M.E. Proteomics applied to the clinical follow-up of patients after allogeneic hematopoietic stem cell transplantation. Blood 2004, 104, 340–349.

33. Taneja, S.; Sen, S.; Gupta, V.K.; Aggarwal, R.; Jameel, S. Plasma and urine biomarkers in acute viral hepatitis E. Proteome Sci. 2009, 7, 39. 

34. Kalantari, S.; Jafari, A.; Moradpoor, R.; Ghasemi, E.; Khalkhal, E. Human urine proteomics: Analytical techniques and clinical applications in renal diseases. Int. J. Proteom. 2015, 2015, 1–17.

35. Fang, X.; Wu, H.; Lu, M.; Cao, Y.; Wang, R.; Wang, M.; Gao, C.; Xia, Z. Urinary proteomics of Henoch-Schönlein purpura nephritis in children using liquid chromatography-tandem mass spectrometry. Clin. Proteom. 2020, 17, 1–11. 

36. Samavat, S.; Kalantari, S.; Nafar, M.; Rutishauser, D.; Rezaei-Tavirani, M.; Parvin, M.; Zubarev, R.A. Diagnostic Urinary Proteome Profile for Immunoglobulin A Nephropathy. Iran. J. Kid. Dis. 2015, 9, 239–248. 

37. Cunningham, R.; Ma, D.; Li, L. Mass spectrometry-based proteomics and peptidomics for systems biology and biomarker discovery. Front. Biol. 2012, 7, 313–335. 

38. Di Meo, A.; Pasic, M.D.; Yousef, G.M. Proteomics and peptidomics: Moving toward precision medicine in urological malignancies. Oncotarget 2016, 7, 52460–52474.

39. Feist, P.; Hummon, A.B. Proteomic Challenges: Sample Preparation Techniques for Microgram-Quantity Protein Analysis from Biological Samples. Int. J. Mol. Sci. 2015, 16, 3537. 

40. Khan, A.; Packer, N. Simple Urinary Sample Preparation for Proteomic Analysis. J. Proteom. Res. 2006, 5, 2824–2838. 

41. Tanaka, T.; Biancotto, A.; Moaddel, R.; Moore, A.Z.; Gonzalez-Freire, M.; Aon, M.A.; Candia, J.; Zhang, P.; Cheung, F.; Fantoni, G.; et al. Plasma proteomic signature of age in healthy humans. Aging Cell 2018, 17, e12799.

42. Shao, C.; Zhao, M.; Chen, X.; Sun, H.; Yang, Y.; Xiao, X.; Guo, Z.; Liu, X.; Lv, Y.; Chen, X.; et al. Comprehensive Analysis of Individual Variation in the Urinary Proteome Revealed Significant Gender Differences. Mol. Cell. Proteom. 2019, 18, 1110–1122.

43. Nkuipou-Kenfack, E.; Bhat, A.; Klein, J.; Jankowski, V.; Mullen, W.; Vlahou, A.; Dakna, M.; Koeck, T.; Schanstra, J.P.; Zürbig, P.; et al. Identification of aging-associated naturally occurring peptides in human urine. Oncotarget 2015, 6, 34106–34117.

44. Mischak, H.; Ioannidis, J.P.; Argiles, A.; Attwood, T.; Bongcam-Rudloff, E.; Brönstrup, M.; Charonis, A.; Chrousos, G.P.; Delles, C.; Dominiczak, A.; et al. Implementation of proteomic biomarkers: Making it work. Eur. J. Clin. Investig. 2012, 42, 1027–1036.

45. Good, D.M.; Zürbig, P.; Argilés, A.; Bauer, H.W.; Behrens, G.; Coon, J.J.; Dakna, M.; Decramer, S.; Delles, C.; Dominiczak, A.F.; et al. Naturally occurring human urinary peptides for use in the diagnosis of chronic kidney disease. Mol. Cell. Proteom. 2010, 9, 2424–2437.

46. Pontillo, C.; Zhang, Z.-Y.; Schanstra, J.P.; Jacobs, L.; Zürbig, P.; Thijs, L.; Ramírez-Torres, A.; Heerspink, H.J.; Lindhardt, M.; Klein, R.; et al. Prediction of Chronic Kidney Disease Stage 3 by CKD273, a Urinary Proteomic Biomarker. Kidney Int. Rep. 2017, 2, 1066–1075. 

47. Catanese, L.; Siwy, J.; Mavrogeorgis, E.; Amann, K.; Mischak, H.; Beige, J.; Rupprecht, H. A Novel Urinary Proteomics Classifier for Non-Invasive Evaluation of Interstitial Fibrosis and Tubular Atrophy in Chronic Kidney Disease. Proteomes 2021, 9, 32. 

48. Pérez, V.; López, D.; Boixadera, E.; Ibernón, M.; Espinal, A.; Bonet, J.; Romero, R. Comparative differential proteomic analysis of minimal change disease and focal segmental glomerulosclerosis. BMC Nephrol. 2017, 18, 1–9.

49. Wang, Y.; Zheng, C.; Wang, X.; Zuo, K.; Liu, Z. Proteomic profile-based screening of potential protein biomarkers in the urine of patients with nephrotic syndrome. Mol. Med. Rep. 2017, 16, 6276–6284.

50. Choi, Y.W.; Kim, Y.G.; Song, M.-Y.; Moon, J.-Y.; Jeong, K.-H.; Lee, T.-W.; Ihm, C.-G.; Park, K.-S.; Lee, S.-H. Potential urine proteomics biomarkers for primary nephrotic syndrome. Clin. Proteom. 2017, 14, 18. 

51. Kalantari, S.; Nafar, M.; Samavat, S.; Rezaei-Tavirani, M.; Rutishauser, D.; Zubarev, R. Urinary Prognostic Biomarkers in Patients With Focal Segmental Glomerulosclerosis. Nephro-Urol. Mon. 2014, 6, e16806. 

52. Nafar, M.; Kalantari, S.; Samavat, S.; Rezaei-Tavirani, M.; Rutishuser, D.; Zubarev, R.A. The novel diagnostic biomarkers for focal segmental Glomerulosclerosis. Int. J. Nephrol. 2014, 2014, 574261.

53. Smith, A.; L’Imperio, V.; De Sio, G.; Ferrario, F.; Scalia, S.; Dell’Antonio, G.; Pierrutzzi, F.; Pontillo, C.; Filip, S.; Markoska, A.; et al. α-1-Antitrypsin detected by MALDI imaging in the study of glomerulonephritis: Its relevance in chronic kidney disease progression. Proteomics 2016, 16, 1759–1766. 

54. Siwy, J.; Zürbig, P.; Argiles, A.; Beige, J.; Haubitz, M.; Jankowski, J.; Julian, B.A.; Linde, P.B.; Marx, D.; Mishkac, H.; et al. Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis. Nephrol. Dial. Transplant. 2017, 32, 2079–2089.

55. Araumi, A.; Osaki, T.; Ichikawa, K.; Kudo, K.; Suzuki, N.; Watanabe, S.; Watanabe, M.; Konta, T. Urinary, and plasma proteomics to discover biomarkers for diagnosing between diabetic nephropathy and minimal change nephrotic syndrome or membranous nephropathy. Biochem. Biophys. Rep. 2021, 27, 101102. 

56. Rood, I.M.; Merchant, M.L.; Wilkey, D.W.; Zhang, T.; Zabrouskov, V.; van der Vlag, J.; Dijikman, H.B.; Wilemens, B.K.; Wetzles, J.F.; Klein, J.B.; et al. Increased expression of lysosome membrane protein 2 in glomeruli of patients with idiopathic membranous nephropathy. Proteomics 2015, 15, 3722–3730. 

57. Pang, L.; Li, Q.; Li, Y.; Liu, Y.; Duan, N.; Li, H. Urine proteomics of primary membranous nephropathy using nanoscale liquid chromatography-tandem mass spectrometry analysis. Clin. Proteom. 2018, 15, 5.

58. Navarro-Muñoz, M.; Ibernon, M.; Bonet, J.; Pérez, V.; Pastor, M.C.; Bayés, B.; Casado-Vela, J.; Navarro, M.; Ara, J.; Espinal, A.; et al. Uromodulin and α1-Antitrypsin Urinary Peptide Analysis to Differentiate Glomerular Kidney Diseases. Kidney Blood Press. Res. 2012, 35, 314–325.

59. Ning, X.; Yin, Z.; Li, Z.; Xu, J.; Wang, L.; Shen, W.; Lu, Y.; Cai, G.; Zhang, X.; Chen, X. Comparative proteomic analysis of urine and laser microdissected glomeruli in IgA nephropathy. Clin. Exp. Pharmacol. Physiol. 2017, 44, 576–585.

60. Guo, Z.; Wang, Z.; Lu, C.; Yang, S.; Sun, H.; Reziw; Guo, Y.; Sun, W.; Yue, H. Analysis of the differential urinary protein profile in IgA nephropathy patients of Uygur ethnicity. BMC Nephrol. 2018, 19, 358. 

61. Prikryl, P.; Vojtova, I.; Maixnerova, D.; Vokurka, M.; Neprasova, M.; Zima, T.; Tesar, V. Proteomic Approach for Identification of IgA Nephropathy-Related Biomarkers in Urine. Physiol. Res. 2017, 66, 621–632.

62. Rudnicki, M.; Siwy, J.; Wendt, R.; Lipphardt, M.; Koziolek, M.J.; Maixnerova, D.; Peters, B.; Kerschbaum, J.; Leierer, J.; Neprasova, M.; et al. Urine proteomics for prediction of disease progression in patients with IgA nephropathy. Nephrol. Dial. Transplant. 2020, 3, 32.

63. Mucha, K.; Bakun, M.; Ja ´zinc, R.; Dadlez, M.; Florczak, M.; Bajor, M.; Gala, K.; P ˛aczek, L. Complement components, proteolysis-related, and cell communication? related proteins detected in urine proteomics are associated with IgA nephropathy. Pol. Arch. Intern. Med. 2014, 124, 380–386.

64. Surin, B.; Sachon, E.; Rougier, J.-P.; Steverlynck, C.; Garreau, C.; Lelongt, B. LG3 fragment of endorepellin is a possible biomarker of severity in IgA nephropathy. Proteomics 2013, 13, 142–152. 

65. Moon, P.G.; Lee, J.E.; You, S.; Kim, T.K.; Cho, J.H.; Kim, I.S.; Kwon, T.-H.; Kim, C.-D.; Park, S.-H.; Hwang, D.; et al. Proteomic analysis of urinary exosomes from patients of early IgA nephropathy and thin basement membrane nephropathy. Proteomics 2011, 11, 2459–2475.

66. Mosley, K.; Tam, F.W.K.; Edwards, R.J.; Crozier, J.; Pusey, C.D.; Lightstone, L. Urinary proteomic profiles distinguish between active and inactive lupus nephritis. Rheumatology 2006, 45, 1497–1504. 

67. Zhang, X.; Jin, M.; Wu, H.; Nadasdy, T.; Nadasdy, G.; Harris, N.; Green-Church, K.; Nagaraja, H.; Birmingham, D.J.; Yu, C.-Y.; et al. Biomarkers of lupus nephritis determined by serial urine proteomics. Kidney Int. 2008, 74, 799–807.

68. Aggarwal, A.; Gupta, R.; Negi, V.S.; Rajasekhar, L.; Misra, R.; Singh, P.; Chaturvedi, V.; Sinha, S. Urinary haptoglobin, alpha-1 anti-chymotrypsin and retinol-binding protein identified by proteomics as potential biomarkers for lupus nephritis. Clin. Exp. Immunol. 2017, 188, 254–262.

69. Turnier, J.L.; Brunner, H.I.; Bennett, M.; Aleed, A.; Gulati, G.; Haffey, W.D.; Thornton, S.; Wagner, M.; Devarajan, P.; Witte, D.; et al. Discovery of SERPINA3 as a candidate urinary biomarker of lupus nephritis activity. Rheumatology 2018, 58, 321–330. 

70. Tailliar, M.; Schanstra, J.; Dierckx, T.; Breuil, B.; Hanouna, G.; Charles, N.; Bascands, J.-L.; Dussol, B.; Vazi, A.; Chiche, L.; et al. Urinary Peptides as Potential Non-Invasive Biomarkers for Lupus Nephritis: Results of the Peptidu-LUP Study. J. Clin. Med. 2021, 10, 1690.

71. Rao, P.V.; Lu, X.; Standley, M.; Pattee, P.; Neelima, G.; Girisesh, G.; Dakshinamurthy, K.V.; Roberts, C.T., Jr.; Nagalla, S.S. Proteomic identification of urinary biomarkers of diabetic nephropathy. Diabetes Care 2007, 30, 629–637.

72. Rossing, K.; Mischak, H.; Dakna, M.; Zürbig, P.; Novak, J.; Julian, B.A.; Good, D.M.; Coon, J.J.; Tarnow, L.; Rossing, P.; et al. Urinary Proteomics in Diabetes and CKD. J. Am. Soc. Nephrol. 2008, 19, 1283–1290.

73. Jin, J.; Gong, J.; Zhao, L.; Li, Y.; Wang, Y.; He, Q. iTRAQ-based comparative proteomics analysis reveals specific urinary biomarkers for various kidney diseases. Biomark. Med. 2020, 14, 839–854.

74. Patel, D.N.; Kalia, K. Characterization of low molecular weight urinary proteins at varying time intervals in type 2 diabetes mellitus and diabetic nephropathy patients. Diabetol. Metab. Syndr. 2019, 11, 39.

75. Liao, W.-L.; Chang, C.-T.; Chen, C.-C.; Lee, W.-J.; Lin, S.-Y.; Liao, H.-Y.; Wu, C.-M.; Chang, Y.-W.; Chen, C.-J.; Tsai, F.-J. Urinary Proteomics for the Early Diagnosis of Diabetic Nephropathy in Taiwanese Patients. J. Clin. Med. 2018, 7, 483. 

76. Chen, C.J.; Liao, W.L.; Chang, C.T.; Liao, H.Y.; Tsai, F.J. Urine proteome analysis by C18 plate-matrix-assisted laser desorption/ionization time-of-flflight mass spectrometry allows non-invasive differential diagnosis and prediction of diabetic nephropathy. PLoS ONE 2018, 13, e0200945.

77. He, T.; Pejchinovski, M.; Mullen, W.; Beige, J.; Mischak, H.; Jankowski, V. Peptides in Plasma, Urine, and Dialysate: Toward Unravelling Renal Peptide Handling. Proteom. Clin. Appl. 2020, 15, e2000029.

78. He, T.; Siwy, J.; Metzger, J.; Mullen, W.; Mischak, H.; Schanstra, J.P.; Zurbin, P.; Jankowski, V. Associations of urinary polymeric immunoglobulin receptor peptides in the context of cardiorenal syndrome. Sci. Rep. 2020, 10, 1–7. 

79. Alkhalaf, A.; Zürbig, P.; Bakker, S.J.L.; Bilo, H.J.G.; Cerna, M.; Fischer, C.; Fuchs, S.; Jannsen, N.; Medek, C.; Miskhac, H.; et al. Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy. PLoS ONE 2010, 5, e13421. 

80. Currie, G.E.; von Scholten, B.J.; Mary, S.; Guerrero, J.-L.F.; Lindhardt, M.; Reinhard, H.; Jacobsen, P.K.; Mullen, W.; Parving, H.-H.; Mischak, H.; et al. Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria. Cardiovasc. Diabetol. 2018, 17, 1–8. 

81. Brondani, L.D.A.; Soares, A.A.; Recamonde-Mendoza, M.; Dall’Agnol, A.; Camargo, J.L.; Monteiro, K.M.; Silveiro, S.P. Urinary peptidomics, and bioinformatics for the detection of diabetic kidney disease. Sci. Rep. 2020, 10, 1–11.

82. Praga, M.; Morales, E.; Herrero, J.C.; Campos, A.P.; Domínguez-Gil, B.; Alegre, R.; Vara, J.; Martínez, M.A. Absence of hypoalbuminemia despite massive proteinuria in focal segmental glomerulosclerosis secondary to hyperfiltration. Am. J. Kidney Dis. 1999, 33, 52–58.

83. Rydel, J.J.; Korbet, S.M.; Borok, R.Z.; Schwartz, M.M. Focal segmental glomerular sclerosis in adults: Presentation, course, and response to treatment. Am. J. Kidney Dis. 1995, 25, 534–542. 

84. D’Agati, V.D.; Fogo, A.B.; Bruijn, J.A.; Jennette, J. Pathologic classification of focal segmental glomerulosclerosis: A working proposal. Am. J. Kidney Dis. 2004, 43, 368–382.

85. Rosenberg, A.Z.; Kopp, J.B. Focal Segmental Glomerulosclerosis. Clin. J. Am. Soc. Nephrol. 2017, 12, 502–517. 

86. Savin, V.J.; Sharma, R.; Sharma, M.; McCarthy, E.T.; Swan, S.K.; Ellis, E.; Lovell, H.; Warady, B.; Gunwar, S.; Chonko, A.M.; et al. Circulating Factor Associated with Increased Glomerular Permeability to Albumin in Recurrent Focal Segmental Glomerulosclerosis. N. Engl. J. Med. 1996, 334, 878–883. 

87. Wei, C.; El Hindi, S.; Li, J.; Fornoni, A.; Goes, N.; Sageshima, J.; Karumanchi, S.A.; Miguel, D.; Yap, H.-K.; Saalem, M.; et al. Circulating urokinase receptor as a cause of focal segmental glomerulosclerosis. Nat. Med. 2011, 17, 952–960.

88. Shankland, S.J.; Pollak, M.R. A suPAR circulating factor causes kidney disease. Nat. Med. 2011, 17, 926–927.

89. Sharma, M.; Zhou, J.; Gauchat, J.-F.; Sharma, R.; McCarthy, E.T.; Srivastava, T.; Savin, V.J. Janus kinase 2/signal transducer and activator of transcription 3 inhibitors attenuate the effect of corticotrophin-like cytokine factor 1 and human focal segmental glomerulosclerosis serum on glomerular filtration barrier. Transl. Res. 2015, 166, 384–398. 

90. Delville, M.; Sigdel, T.K.; Wei, C.; Li, J.; Hsieh, S.-C.; Fornoni, A.; Burke, G.W.; Bruneval, P.; Naesens, M.; Jackson, A.; et al. A circulating antibody panel for pretransplant prediction of FSGS recurrence after kidney transplantation. Sci. Transl. Med. 2014, 6, 256ra136. 

91. Yu, C.-C.; Fornoni, A.; Weins, A.; Hakroush, S.; Maiguel, D.; Sageshima, J.; Chen, L.; Ciancio, G.; Faridi, M.H.; Behr, D.; et al. Abatacept in B7-1–Positive Proteinuric Kidney Disease. N. Engl. J. Med. 2013, 369, 2416–2423.

92. Korbet, S.M.; Schwartz, M.M.; Lewis, E.J. Primary Focal Segmental Glomerulosclerosis: Clinical Course and Response to Therapy. Am. J. Kidney Dis. 1994, 23, 773–783.

93. Wehrmann, M.; Bohle, A.; Held, H.; Schumm, G.; Kendziorra, H.; Pressler, H. Long-term prognosis of focal sclerosing glomerulonephritis. An analysis of 250 cases with particular regard to tubulointerstitial changes. Clin. Nephrol. 1990, 33, 115–122. 

94. Merchant, M.L.; Barati, M.T.; Caster, D.J.; Hata, J.L.; Hobeika, L.; Coventry, S.; Brier, M.E.; Wilkey, D.W.; Li, M.; Rood, I.M.; et al. Proteomic Analysis Identifies Distinct Glomerular Extracellular Matrix in Collapsing Focal Segmental Glomerulosclerosis. J. Am. Soc. Nephrol. 2020, 31, 1883–1904.

95. Schwaller, B. Calretinin: From a “simple” Ca2+ buffer to a multifunctional protein implicated in many biological processes. Front. Neuroanat. 2014, 8, 3.

96. Beeken, M.; Lindenmeyer, M.T.; Blattner, S.M.; Radón, V.; Oh, J.; Meyer, T.N.; Hildebrand, D.; Schlüter, H.; Reinicke, A.T.; Knop, J.-H.; et al. Alterations in the Ubiquitin Proteasome System in Persistent but Not Reversible Proteinuric Diseases. J. Am. Soc. Nephrol. 2014, 25, 2511–2525. 

97. Meyer-Schwesinger, C.; Meyer, T.; Münster, S.; Klug, P.; Saleem, M.; Helmchen, U.; Stahl, R. A new role for the neuronal ubiquitin C-terminal hydrolase-L1 (UCH-L1) in podocyte process formation and podocyte injury in human glomerulopathies. J. Pathol. 2009, 217, 452–464. 

98. Meyer-Schwesinger, C.; Meyer, T.N.; Sievert, H.; Hoxha, E.; Sachs, M.; Klupp, E.M.; Munster, S.; Balabanov, S.; Carrier, L.; Helmchen, U.; et al. Ubiquitin C-terminal hydro-lase-l1 activity induces polyubiquitin accumulation in podocytes and increases proteinuria in rat membranous nephropathy. Am. J. Pathol. 2011, 178, 2044–2057. 

99. Moroni, G.; Ponticelli, C. Secondary Membranous Nephropathy. A Narrative Review. Front. Med. 2020, 7. 

100. Ligabue, G.; Magistroni, R.; Cantu’, M.; Genovese, F.; Lupo, V.; Cavazzini, F.; Furci, L.; Cappelli, F. Identifification and Characterization of New Proteins in Podocyte Dysfunction of Membranous Nephropathy by Proteomic Analysis of Renal Biopsy. Curr. Pharmacogen. Person. Med. 2013, 11, 42–52. 

101. Dieplinger, H.; Dieplinger, B. Afamin—A pleiotropic glycoprotein involved in various disease states. Clin. Chim. Acta 2015, 446, 105–110. 

102. McGrogan, A.; Franssen, C.F.; de Vries, C.S. The incidence of primary glomerulonephritis worldwide: A systematic review of the literature. Nephrol. Dial. Transplant. 2011, 26, 414–430. 

103. Zaza, G.; Bernich, P.; Lupo, A.; Triveneto’ Register of Renal Biopsies (TVRRB). Incidence of primary glomerulonephritis in a large North-Eastern Italian area: A 13-year renal biopsy study. Nephrol. Dial. Transplant. 2013, 28, 367–372.

104. Maixnerova, D.; Bauerova, L.; Skibova, J.; Rysava, R.; Reiterova, J.; Merta, M.; Honsova, E.; Tesar, V. The retrospective analysis of 343 Czech patients with IgA nephropathy—One center experience. Nephrol. Dial. Transplant. 2012, 27, 1492–1498.

105. Suzuki, H.; Kiryluk, K.; Novak, J.; Moldoveanu, Z.; Herr, A.; Renfrow, M.B.; Wyatt, R.; Scolari, F.; Mestecky, J.; Gharavi, A.G.; et al. The Pathophysiology of IgA Nephropathy. J. Am. Soc. Nephrol. 2011, 22, 1795–1803. 

106. Zhang, W.; Lachmann, P.J. Glycosylation of IgA is required for optimal activation of the alternative complement pathway by immune complexes. Immunology 1994, 81, 137–141. 

107. Moura, I.C.; Arcos-Fajardo, M.; Gdoura, A.; Leroy, V.; Sadaka, C.; Mahlaoui, N.; Yves, L.; Vrtovnski, F.; Haddad, E.; Benhamou, M.; et al. Engagement of transferrin receptor by polymeric IgA1: Evidence for a positive feedback loop involving increased receptor expression and mesangial cell proliferation in IgA nephropathy. JASN 2005, 16, 2667–2676. 

108. Majd, T.M.; Kalantari, S.; Shahraki, H.R.; Nafar, M.; Almasi, A.; Samavat, S.; Parvin, M.; Hashemian, A. Application of sparse linear discriminant analysis and elastic net for diagnosis of IgA nephropathy: Statistical and biological viewpoints. Iran. Biomed. J. 2018, 22, 374–384. 

109. Johnson, R.J.; Feehally, J.; Floege, J. Comprehensive Clinical Nephrology; Elsevier Health Sciences: Amsterdam, The Netherlands, 2019.

110. Guillén-Gómez, E.; de Quixano, B.B.; Ferrer, S.; Brotons, C.; Knepper, M.A.; Carrascal, M.; Abian, J.; Mas, J.M.; Calero, F.; Ballarín, J.A.; et al. Urinary Proteome Analysis Identifies Neprilysin and VCAM as Proteins Involved in Diabetic Nephropathy. J. Diabetes Res. 2018, 2018, 1–12.

111. Ahn, H.-S.; Kim, J.H.; Jeong, H.; Yu, J.; Yeom, J.; Song, S.H.; Kim, S.S.; Kim, I.J.; Kim, K. Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction. Int. J. Mol. Sci. 2020, 21, 4236. 

112. Musa, R.; Brent, L.H.; Qurie, A. Lupus Nephritis; Stat Pearls Publishing: Treasure Island, FL, USA, 2021. 

113. 109Devuyst, O.; Bochud, M. Uromodulin, kidney function, cardiovascular disease, and mortality. Kidney Int. 2015, 88, 944–946. 

114. Trudu, M.; Janas, S.; Lanzani, C.; Debaix, H.; Schaeffer, C.; Ikehata, M.; Citterio, L.; Demaretz, C.; Trevisani, F.; Ristango, G.; et al. Common noncoding UMOD gene variants induce salt-sensitive hypertension and kidney damage by increasing uromodulin expression. Nat. Med. 2013, 19, 1655–1660. 

115. Jamin, A.; Berthelot, L.; Couderc, A.; Chemouny, J.M.; Boedec, E.; Dehoux, L.; Abbad, L.; Dossier, C.; Daugas, E.; Monteiro, R.; et al. Autoantibodies against podocytes UCHL1 are associated with idiopathic nephrotic syndrome relapses and induce proteinuria in mice. J. Autoimmun. 2018, 89, 149–161. 

116. Bruschi, M.; Catarsi, P.; Candiano, G.; Pia, M.; Rastaldi, M.P.; Musante, L.; Scolari, F.; Artero, M.; Carraro, M.; Carrea, A.; et al. Apolipoprotein E in idiopathic nephrotic syndrome and focal segmental glomerulosclerosis. Kidney Int. 2003, 63, 686–695. 

117. Marek-Bukowiec, K.; Konieczny, A.; Ratajczyk, K.; Macur, K.; Czaplewska, P.; Czy˙zewska-Buczy ´nska, A.; Kowal, P.; Witkiewicz, W. The value of urinary RBP4 in the diagnosis of FSGS and other renal diseases. Trends Biomed. Res. 2020, 3.

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