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An integrated proteomic and peptidomic assessment of the normal human urinome

  • Ashley Di Meo , Ihor Batruch , Arsani G. Yousef , Maria D. Pasic , Eleftherios P. Diamandis and George M. Yousef EMAIL logo
Published/Copyright: July 9, 2016
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 55 Issue 2

Abstract

Background:

Urine represents an ideal source of clinically relevant biomarkers as it contains a large number of proteins and low molecular weight peptides. The comprehensive characterization of the normal urinary proteome and peptidome can serve as a reference for future biomarker discovery. Proteomic and peptidomic analysis of urine can also provide insight into normal physiology and disease pathology, especially for urogenital diseases.

Methods:

We developed an integrated proteomic and peptidomic analytical protocol in normal urine. We employed ultrafiltration to separate protein and peptide fractions, which were analyzed separately using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on the Q-Exactive mass spectrometer.

Results:

By analyzing six urines from healthy individuals with advanced age, we identified 1754 proteins by proteomic analysis and 4543 endogenous peptides, arising from 566 proteins by peptidomic analysis. Overall, we identified 2091 non-redundant proteins by this integrated approach. In silico protease activity analysis indicated that metalloproteases are predominantly involved in the generation of the endogenous peptide signature. In addition, a number of proteins that were detected in normal urine have previously been implicated in various urological malignancies, including bladder cancer and renal cell carcinoma (RCC).

Conclusions:

We utilized a highly sensitive proteomics approach that enabled us to identify one of the largest sets of protein identifications documented in normal human urine. The raw proteomics and peptidomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD003595.

Keywords: mass spectrometry; peptide sequence alignment; protease activity analysis; urinary peptidome; urinary proteome
Corresponding author: George M. Yousef, MD, PhD, FRCPC (Path), Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond Street, Toronto, ON, M5B 1W8, Canada, Phone: +1-416-864-6060, Ext: 77605, Fax: +416-864-5648

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work was supported by grants from the Canadian Institute of Health Research (MOP 119606), Kidney Foundation of Canada (KFOC130030), Kidney Cancer Research Network of Canada and Prostate Cancer Canada Movember Discovery Grants (D2013-39).

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

1. Blaine J, Chonchol M, Levi M. Renal control of calcium, phosphate, and magnesium homeostasis. Clin J Am Soc Nephrol 2015;10:1257–72.10.2215/CJN.09750913Search in Google Scholar PubMed PubMed Central

2. Tojo A. The role of the kidney in protein metabolism: the capacity of tubular lysosomal proteolysis in nephrotic syndrome. Kidney Int 2013;84:861–3.10.1038/ki.2013.284Search in Google Scholar PubMed

3. Bauca JM, Martinez-Morillo E, Diamandis EP. Peptidomics of urine and other biofluids for cancer diagnostics. Clin Chem 2014;60:1052–61.10.1373/clinchem.2013.211714Search in Google Scholar PubMed

4. Dallas DC, Guerrero A, Parker EA, Robinson RC, Gan J, German JB, et al. Current peptidomics: applications, purification, identification, quantification, and functional analysis. Proteomics 2015;15:1026–38.10.1002/pmic.201400310Search in Google Scholar PubMed PubMed Central

5. Jurgens M, Appel A, Heine G, Neitz S, Menzel C, Tammen H, et al. Towards characterization of the human urinary peptidome. Comb Chem High Throughput Screen 2005;8:757–65.10.2174/138620705774962364Search in Google Scholar PubMed

6. Di Meo A, Pasic MD, Yousef GM. Proteomics and peptidomics: moving toward precision medicine in urological malignancies. Oncotarget 2016. 10.18632/oncotarget.8931.Search in Google Scholar PubMed PubMed Central

7. Sigdel TK, Salomonis N, Nicora CD, Ryu S, He J, Dinh V, et al. The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics. Mol Cell Proteomics 2014;13:621–31.10.1074/mcp.M113.030577Search in Google Scholar PubMed PubMed Central

8. Frantzi M, Metzger J, Banks RE, Husi H, Klein J, Dakna M, et al. Discovery and validation of urinary biomarkers for detection of renal cell carcinoma. J Proteomics 2014;98:44–58.10.1016/j.jprot.2013.12.010Search in Google Scholar PubMed

9. Rocchetti MT, Centra M, Papale M, Bortone G, Palermo C, Centonze D, et al. Urine protein profile of IgA nephropathy patients may predict the response to ACE-inhibitor therapy. Proteomics 2008;8:206–16.10.1002/pmic.200700492Search in Google Scholar PubMed

10. Davalieva K, Kiprijanovska S, Komina S, Petrusevska G, Zografska NC, Polenakovic M. Proteomics analysis of urine reveals acute phase response proteins as candidate diagnostic biomarkers for prostate cancer. Proteome Sci 2015;13:2.10.1186/s12953-014-0059-9Search in Google Scholar PubMed PubMed Central

11. Smith CR, Batruch I, Bauca JM, Kosanam H, Ridley J, Bernardini MQ, et al. Deciphering the peptidome of urine from ovarian cancer patients and healthy controls. Clin Proteomics 2014;11:23.10.1186/1559-0275-11-23Search in Google Scholar PubMed PubMed Central

12. Frantzi M, Latosinska A, Fluhe L, Hupe MC, Critselis E, Kramer MW, et al. Developing proteomic biomarkers for bladder cancer: towards clinical application. Nat Rev Urol 2015;12:317–30.10.1038/nrurol.2015.100Search in Google Scholar PubMed

13. Davis MT, Auger PL, Patterson SD. Cancer biomarker discovery via low molecular weight serum profiling--are we following circular paths? Clin Chem 2010;56:244–7.10.1373/clinchem.2009.127951Search in Google Scholar PubMed

14. Good DM, Thongboonkerd V, Novak J, Bascands JL, Schanstra JP, Coon JJ, et al. Body fluid proteomics for biomarker discovery: lessons from the past hold the key to success in the future. J Proteome Res 2007;6:4549–55.10.1021/pr070529wSearch in Google Scholar PubMed

15. Mischak H, Julian BA, Novak J. High-resolution proteome/peptidome analysis of peptides and low-molecular-weight proteins in urine. Proteomics Clin Appl 2007;1:792.10.1002/prca.200700043Search in Google Scholar PubMed PubMed Central

16. Thongboonkerd V, McLeish KR, Arthur JM, Klein JB. Proteomic analysis of normal human urinary proteins isolated by acetone precipitation or ultracentrifugation. Kidney Int 2002;62: 1461–9.10.1111/j.1523-1755.2002.kid565.xSearch in Google Scholar PubMed

17. Pieper R, Gatlin CL, McGrath AM, Makusky AJ, Mondal M, Seonarain M, et al. Characterization of the human urinary proteome: a method for high-resolution display of urinary proteins on two-dimensional electrophoresis gels with a yield of nearly 1400 distinct protein spots. Proteomics 2004;4:1159–74.10.1002/pmic.200300661Search in Google Scholar PubMed

18. Sun W, Li F, Wu S, Wang X, Zheng D, Wang J, et al. Human urine proteome analysis by three separation approaches. Proteomics 2005;5:4994–5001.10.1002/pmic.200401334Search in Google Scholar PubMed

19. Li QR, Fan KX, Li RX, Dai J, Wu CC, Zhao SL, et al. A comprehensive and non-prefractionation on the protein level approach for the human urinary proteome: touching phosphorylation in urine. Rapid Commun Mass Spectrom 2010;24:823–32.10.1002/rcm.4441Search in Google Scholar PubMed

20. Adachi J, Kumar C, Zhang Y, Olsen JV, Mann M. The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biol 2006;7:R80.10.1186/gb-2006-7-9-r80Search in Google Scholar

21. Marimuthu A, O'Meally RN, Chaerkady R, Subbannayya Y, Nanjappa V, Kumar P, et al. A comprehensive map of the human urinary proteome. J Proteome Res 2011;10:2734–43.10.1021/pr2003038Search in Google Scholar PubMed PubMed Central

22. Santucci L, Candiano G, Petretto A, Bruschi M, Lavarello C, Inglese E, et al. From hundreds to thousands: Widening the normal human Urinome (1). J Proteomics 2015;112:53–62.10.1016/j.jprot.2014.07.021Search in Google Scholar PubMed

23. Santucci L, Candiano G, Petretto A, Bruschi M, Lavarello C, Inglese E, et al. From hundreds to thousands: Widening the normal human Urinome. Data Brief 2014;1:25–8.10.1016/j.dib.2014.08.006Search in Google Scholar PubMed PubMed Central

24. Yang X, Hu L, Ye M, Zou H. Analysis of the human urine endogenous peptides by nanoparticle extraction and mass spectrometry identification. Anal Chim Acta 2014;829:40–7.10.1016/j.aca.2014.04.040Search in Google Scholar PubMed

25. Fiedler GM, Baumann S, Leichtle A, Oltmann A, Kase J, Thiery J, et al. Standardized peptidome profiling of human urine by magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Clin Chem 2007;53:421–8.10.1373/clinchem.2006.077834Search in Google Scholar PubMed

26. Mischak H, Kolch W, Aivaliotis M, Bouyssie D, Court M, Dihazi H, et al. Comprehensive human urine standards for comparability and standardization in clinical proteome analysis. Proteomics Clin Appl 2010;4:464–78.10.1002/prca.200900189Search in Google Scholar PubMed PubMed Central

27. Liu X, Chinello C, Musante L, Cazzaniga M, Tataruch D, Calzaferri G, et al. Intraluminal proteome and peptidome of human urinary extracellular vesicles. Proteomics Clin Appl 2015;9:568–73.10.1002/prca.201400085Search in Google Scholar PubMed

28. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 2011;10:1794–805.10.1021/pr101065jSearch in Google Scholar PubMed

29. Luber CA, Cox J, Lauterbach H, Fancke B, Selbach M, Tschopp J, et al. Quantitative proteomics reveals subset-specific viral recognition in dendritic cells. Immunity 2010;32:279–89.10.1016/j.immuni.2010.01.013Search in Google Scholar PubMed

30. Cox J, Hubner NC, Mann M. How much peptide sequence information is contained in ion trap tandem mass spectra? J Am Soc Mass Spectrom 2008;19:1813–20.10.1016/j.jasms.2008.07.024Search in Google Scholar PubMed

31. Klein J, Eales J, Zurbig P, Vlahou A, Mischak H, Stevens R. Proteasix: a tool for automated and large-scale prediction of proteases involved in naturally occurring peptide generation. Proteomics 2013;13:1077–82.10.1002/pmic.201200493Search in Google Scholar PubMed

32. Guerrero A, Dallas DC, Contreras S, Chee S, Parker EA, Sun X, et al. Mechanistic peptidomics: factors that dictate specificity in the formation of endogenous peptides in human milk. Mol Cell Proteomics 2014;13:3343–51.10.1074/mcp.M113.036194Search in Google Scholar PubMed PubMed Central

33. Nagaraj N, Mann M. Quantitative analysis of the intra- and inter-individual variability of the normal urinary proteome. J Proteome Res 2011;10:637–45.10.1021/pr100835sSearch in Google Scholar PubMed

34. Mutowo-Meullenet P, Huntley RP, Dimmer EC, Alam-Faruque Y, Sawford T, Jesus Martin M, et al. Use of Gene Ontology Annotation to understand the peroxisome proteome in humans. Database (Oxford) 2013;2013:bas062.10.1093/database/bas062Search in Google Scholar PubMed PubMed Central

35. Lachmann PJ. Lupus and desoxyribonuclease. Lupus 2003;12:202–6.10.1191/0961203303lu357xxSearch in Google Scholar PubMed

36. Bakun M, Senatorski G, Rubel T, Lukasik A, Zielenkiewicz P, Dadlez M, et al. Urine proteomes of healthy aging humans reveal extracellular matrix (ECM) alterations and immune system dysfunction. Age 2014;36:299–311.10.1007/s11357-013-9562-7Search in Google Scholar PubMed PubMed Central

37. Liu KQ, Liu ZP, Hao JK, Chen L, Zhao XM. Identifying dysregulated pathways in cancers from pathway interaction networks. BMC Bioinformatics 2012;13:126.10.1186/1471-2105-13-126Search in Google Scholar PubMed PubMed Central

38. Overall CM, Blobel CP. In search of partners: linking extracellular proteases to substrates. Nat Rev Mol Cell Biol 2007;8:245–57.10.1038/nrm2120Search in Google Scholar PubMed

39. Lyons PJ, Fricker LD. Peptidomic approaches to study proteolytic activity. Curr Protoc Protein Sci 2011;Chapter 18:Unit 18.13.10.1002/0471140864.ps1813s65Search in Google Scholar PubMed PubMed Central

40. Page-McCaw A, Ewald AJ, Werb Z. Matrix metalloproteinases and the regulation of tissue remodelling. Nat Rev Mol Cell Biol 2007;8:221–33.10.1038/nrm2125Search in Google Scholar PubMed PubMed Central

41. Vu TH, Werb Z. Matrix metalloproteinases: effectors of development and normal physiology. Genes Dev 2000;14:2123–33.10.1101/gad.815400Search in Google Scholar PubMed

42. Klein T, Bischoff R. Physiology and pathophysiology of matrix metalloproteases. Amino Acids 2011;41:271–90.10.1007/s00726-010-0689-xSearch in Google Scholar PubMed PubMed Central

43. Turk B. Targeting proteases: successes, failures and future prospects. Nat Rev Drug Discov 2006;5:785–99.10.1038/nrd2092Search in Google Scholar PubMed

44. Szarvas T, vom Dorp F, Ergun S, Rubben H. Matrix metalloproteinases and their clinical relevance in urinary bladder cancer. Nat Rev Urol 2011;8:241–54.10.1038/nrurol.2011.44Search in Google Scholar PubMed

45. Barkan DT, Hostetter DR, Mahrus S, Pieper U, Wells JA, Craik CS, et al. Prediction of protease substrates using sequence and structure features. Bioinformatics 2010;26:1714–22.10.1093/bioinformatics/btq267Search in Google Scholar PubMed PubMed Central

46. Chen YT, Chen CL, Chen HW, Chung T, Wu CC, Chen CD, et al. Discovery of novel bladder cancer biomarkers by comparative urine proteomics using iTRAQ technology. J Proteome Res 2010;9:5803–15.10.1021/pr100576xSearch in Google Scholar PubMed

47. Chen CL, Lin TS, Tsai CH, Wu CC, Chung T, Chien KY, et al. Identification of potential bladder cancer markers in urine by abundant-protein depletion coupled with quantitative proteomics. J Proteomics 2013;85:28–43.10.1016/j.jprot.2013.04.024Search in Google Scholar PubMed

48. Kawata N, Nagane Y, Hirakata H, Ichinose T, Okada Y, Yamaguchi K, et al. Significant relationship of matrix metalloproteinase 9 with nuclear grade and prognostic impact of tissue inhibitor of metalloproteinase 2 for incidental clear cell renal cell carcinoma. Urology 2007;69:1049–53.10.1016/j.urology.2007.02.044Search in Google Scholar PubMed

49. Sato A, Nagase H, Obinata D, Fujiwara K, Fukuda N, Soma M, et al. Inhibition of MMP-9 using a pyrrole-imidazole polyamide reduces cell invasion in renal cell carcinoma. Int J Oncol 2013;43:1441–6.10.3892/ijo.2013.2073Search in Google Scholar PubMed

50. Gaut JP, Crimmins DL, Lockwood CM, McQuillan JJ, Ladenson JH. Expression of the Na+/K+-transporting ATPase gamma subunit FXYD2 in renal tumors. Mod Pathol 2013;26:716–24.10.1038/modpathol.2012.202Search in Google Scholar PubMed

51. Langner C, Ratschek M, Rehak P, Schips L, Zigeuner R. Expression of MUC1 (EMA) and E-cadherin in renal cell carcinoma: a systematic immunohistochemical analysis of 188 cases. Mod Pathol 2004;17:180–8.10.1038/modpathol.3800032Search in Google Scholar PubMed

52. Aubert S, Fauquette V, Hemon B, Lepoivre R, Briez N, Bernard D, et al. MUC1, a new hypoxia inducible factor target gene, is an actor in clear renal cell carcinoma tumor progression. Cancer Res 2009;69:5707–15.10.1158/0008-5472.CAN-08-4905Search in Google Scholar PubMed

53. Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics 2014;13:2513–26.10.1074/mcp.M113.031591Search in Google Scholar PubMed PubMed Central

54. Romanova EV, Dowd SE, Sweedler JV. Quantitation of endogenous peptides using mass spectrometry based methods. Curr Opin Chem Biol 2013;17:801–8.10.1016/j.cbpa.2013.05.030Search in Google Scholar PubMed PubMed Central

55. Olsen JV, Mann M. Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol Cell Proteomics 2013;12:3444–52.10.1074/mcp.O113.034181Search in Google Scholar PubMed PubMed Central

56. Thompson RH, Ordonez MA, Iasonos A, Secin FP, Guillonneau B, Russo P, et al. Renal cell carcinoma in young and old patients–is there a difference? J Urol 2008;180:1262–6; discussion 6.10.1016/j.juro.2008.06.037Search in Google Scholar PubMed PubMed Central

57. Taylor JA, 3rd, Kuchel GA. Bladder cancer in the elderly: clinical outcomes, basic mechanisms, and future research direction. Nat Clin Pract Urol 2009;6:135–44.10.1038/ncpuro1315Search in Google Scholar PubMed PubMed Central

58. Nkuipou-Kenfack E, Bhat A, Klein J, Jankowski V, Mullen W, Vlahou A, et al. Identification of ageing-associated naturally occurring peptides in human urine. Oncotarget 2015;6: 34106–17.10.18632/oncotarget.5896Search in Google Scholar PubMed PubMed Central

Supplemental Material:

The online version of this article (DOI: 10.1515/cclm-2016-0390) offers supplementary material, available to authorized users.

Received: 2016-5-3
Accepted: 2016-6-9
Published Online: 2016-7-9
Published in Print: 2017-2-1

©2017 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2016-0390
Keywords for this article
mass spectrometry; peptide sequence alignment; protease activity analysis; urinary peptidome; urinary proteome

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Commutable samples with assigned target concentrations may help us harmonise general chemistry results
  4. Reviews
  5. Performance of point-of-care HbA1c test devices: implications for use in clinical practice – a systematic review and meta-analysis
  6. Cardiac troponins and mortality in type 1 and 2 myocardial infarction
  7. Opinion Paper
  8. Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM Strategic Conference
  9. Genetics and Molecular Diagnostics
  10. External quality assessment for human papillomavirus 16/18 DNA detection and genotyping in Shanghai, China
  11. General Clinical Chemistry and Laboratory Medicine
  12. Analytical performance of 17 general chemistry analytes across countries and across manufacturers in the INPUtS project of EQA organizers in Italy, the Netherlands, Portugal, United Kingdom and Spain
  13. Commutability of proficiency testing material containing tobramycin: a study within the framework of the Dutch Calibration 2.000 project
  14. Optimization and validation of moving average quality control procedures using bias detection curves and moving average validation charts
  15. Extending laboratory automation to the wards: effect of an innovative pneumatic tube system on diagnostic samples and transport time
  16. Smart management of sample dilution using an artificial neural network to achieve streamlined processes and saving resources: the automated nephelometric testing of serum free light chain as case study
  17. An integrated proteomic and peptidomic assessment of the normal human urinome
  18. An alternative inhibition method for determining cross-reactive allergens
  19. Validation of a new assay for α-synuclein detection in cerebrospinal fluid
  20. Reference Values and Biological Variations
  21. Intra-individual variation of plasma trimethylamine-N-oxide (TMAO), betaine and choline over 1 year
  22. Cancer Diagnostics
  23. Predictive performance of TPA testing for recurrent disease during follow-up after curative intent surgery for colorectal carcinoma
  24. Cardiovascular Diseases
  25. Mid-regional pro-adrenomedullin (MR-proADM) and mid-regional pro-atrial natriuretic peptide (MR-proANP) in severe aortic valve stenosis: association with outcome after transcatheter aortic valve implantation (TAVI)
  26. Association between apolipoprotein E polymorphisms and premature coronary artery disease: a meta-analysis
  27. Urinary orosomucoid: a novel, early biomarker of sepsis with promising diagnostic performance
  28. Letters to the Editor
  29. CT or MRI
  30. Reply to: CT or MRI in the diagnosis of right lower quadrant abdominal pain?
  31. Quantification of daratumumab in the serum protein electrophoresis
  32. Response to: Interference of daratumumab on the serum protein electrophoresis
  33. Glycated albumin: correlation to HbA1c and preliminary reference interval evaluation
  34. Using “big data” to describe the effect of seasonal variation in thyroid-stimulating hormone
  35. IgE multiple myeloma: a new case report
  36. Therapeutic decision-making process in the intensive care unit: role of biological point-of-care testing
  37. How can we evaluate differences between serial measurements on the same sample? A new approach based on within-subject biological variation
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