Abstract
Urine is a sample of choice for noninvasive biomarkers search because it is easily available in large amounts and its molecular composition provides information on processes in the organism. The high potential of urine peptidomics has been demonstrated for clinical purpose. Several mass spectrometry based approaches have been successfully applied for urine peptidome analysis and potential biomarkers search. Summarizing literature data and our own experience we developed a protocol for comprehensive urine peptidome analysis. The technology includes several stages and consists of urine sample preparation by size exclusion chromatography and identification of featured peptides by nano-HPLC coupled to Fourier transform ion cyclotron resonance mass spectrometry, semiquantitative and statistical data analysis.
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References
Filip S, Pontillo C, Peter Schanstra J et al (2014) Urinary proteomics and molecular determinants of chronic kidney disease: possible link to proteases. Expert Rev Proteomics 11:535–548. https://doi.org/10.1586/14789450.2014.926224
Schrader M, Schulz-Knappe P (2001) Peptidomics technologies for human body fluids. Trends Biotechnol 19:S55–S60. https://doi.org/10.1016/S0167-7799(01)01800-5
Ivanov VT, Yatskin ON (2005) Peptidomics: a logical sequel to proteomics. Expert Rev Proteomics 2:463–473. https://doi.org/10.1586/14789450.2.4.463
Schrader M, Schulz-Knappe P, Fricker LD (2014) Historical perspective of peptidomics. EuPA Open Proteom 3:171–182. https://doi.org/10.1016/j.euprot.2014.02.014
Machtejevas E, Marko-Varga G, Lindberg C et al (2009) Profiling of endogenous peptides by multidimensional liquid chromatography: on-line automated sample cleanup for biomarker discovery in human urine. J Sep Sci 32:2223–2232. https://doi.org/10.1002/jssc.200900058
Yang X, Hu L, Ye M et al (2014) Analysis of the human urine endogenous peptides by nanoparticle extraction and mass spectrometry identification. Anal Chim Acta 829:40–47. https://doi.org/10.1016/j.aca.2014.04.040
Broker ME, Lalmahomed ZS, Roest HP et al (2013) Collagen peptides in urine: a new promising biomarker for the detection of colorectal liver metastases. PLoS One 8:e70918. https://doi.org/10.1371/journal.pone.0070918
Cutillas PR, Norden AG, Cramer R et al (2003) Detection and analysis of urinary peptides by on-line liquid chromatography and mass spectrometry: application to patients with renal Fanconi syndrome. Clin Sci 104:483–490. https://doi.org/10.1042/CS20020342
Ling XB, Lau K, Deshpande C et al (2010) Urine peptidomic and targeted plasma protein analyses in the diagnosis and monitoring of systemic juvenile idiopathic arthritis. Clin Proteomics 6:175–193. https://doi.org/10.1007/s12014-010-9058-8
Ling XB, Lau K, Kanegaye JT et al (2011) A diagnostic algorithm combining clinical and molecular data distinguishes Kawasaki disease from other febrile illnesses. BMC Med 9:130. https://doi.org/10.1186/1741-7015-9-130
Nkuipou-Kenfack E, Zurbig P, Mischak H (2016) The long path towards implementation of clinical proteomics: exemplified based on CKD273. Proteomics Clin Appl 11:1600104. https://doi.org/10.1002/prca.201600104
Buhimschi IA, Zhao G, Funai EF et al (2008) Proteomic profiling of urine identifies specific fragments of SERPINA1 and albumin as biomarkers of preeclampsia. Am J Obstet Gynecol 199:551.e1–551.16. https://doi.org/10.1016/j.ajog.2008.07.006
Carty DM, Siwy J, Brennand JE et al (2011) Urinary proteomics for prediction of preeclampsia. Hypertension 57:561–569. https://doi.org/10.1161/HYPERTENSIONAHA.110.164285
Kononikhin AS, Starodubtseva NL, Bugrova AE et al (2016) An untargeted approach for the analysis of the urine peptidome of women with preeclampsia. J Proteome 149:38–43. https://doi.org/10.1016/j.jprot.2016.04.024
Klein J, Papadopoulos T, Mischak H et al (2014) Comparison of CE-MS/MS and LC-MS/MS sequencing demonstrates significant complementarity in natural peptide identification in human urine. Electrophoresis 35:1060–1064. https://doi.org/10.1002/elps.201300327
Thevenot EA, Roux A, Xu Y et al (2015) Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J Proteome Res 14:3322–3335. https://doi.org/10.1021/acs.jproteome.5b00354
Dunn OJ (1961) Multiple comparisons among means. J Am Stat Assoc 56:52–64. https://doi.org/10.1080/01621459.1961.10482090
Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60. https://doi.org/10.1214/aoms/1177730491
Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47:583–621. https://doi.org/10.2307/2280779
Acknowledgments
The study was supported by RFBR grants no. 16-54-21011_SNF_а, 17-08-01537 A and SNF grant no. SNF IZLRZ3_163911.
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Kononikhin, A.S. et al. (2018). Methodology for Urine Peptidome Analysis Based on Nano-HPLC Coupled to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. In: Schrader, M., Fricker, L. (eds) Peptidomics. Methods in Molecular Biology, vol 1719. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7537-2_20
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DOI: https://doi.org/10.1007/978-1-4939-7537-2_20
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