Urine metabolome investigation in pediatric urology. Review
- Authors: Kuzovleva G.I.1,2, Vlasenko E.Y.1, Maltseva L.D.1, Morozova O.L.1
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Affiliations:
- I.M. Sechenov First Moscow State Medical University (Sechenov University)
- Speransky Children’s Hospital No. 9
- Issue: Vol 13, No 4 (2023)
- Pages: 551-563
- Section: Reviews
- URL: https://journals.rcsi.science/2219-4061/article/view/249864
- DOI: https://doi.org/10.17816/psaic1546
- ID: 249864
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Abstract
Metabolomics is the science of studying small molecules (50–5,000 Da) formed because of the implementation of metabolic pathways in cells and the maintenance of their vital functions. The study of urine metabolome is a promising direction for diagnosing early stages of damage to various cells of the urinary system in pediatric urology, allowing the study of biomarkers or their spectrum, which can improve the identification of existing disorders, and multivariate analysis will provide greater accuracy in making a diagnosis. This study aimed to summarize existing information on urine metabolome and its changes in cases of congenital malformations of the urinary system, accompanied by renal dysplasia, leading to acute kidney injury or chronic kidney disease. A literature search and review was conducted using PubMed, Embase, and Google Scholar. The review presents the possibilities of metabolomic analysis to provide a qualitatively new level of diagnosis and monitoring of damage to the structures of organs and tissues of the urinary system, identifying predictors of pathology progression, and personalized techniques for making medical decisions. However, this method is limited by the high cost of the equipment, need for training of highly qualified personnel, and difficulty in interpreting the results. The study of urine metabolome is very promising for the diagnosis and selection of a timely, rational treatment strategy for children with malformations of the urinary system.
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##article.viewOnOriginalSite##About the authors
Galina I. Kuzovleva
I.M. Sechenov First Moscow State Medical University (Sechenov University); Speransky Children’s Hospital No. 9
Author for correspondence.
Email: dr.gala@mail.ru
ORCID iD: 0000-0002-5957-7037
SPIN-code: 7990-4317
MD, Cand. Sci. (Med.)
Russian Federation, Moscow; MoscowEkaterina Yu. Vlasenko
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Email: vlasenko.ekaterina@icloud.com
ORCID iD: 0000-0002-3138-8314
SPIN-code: 8290-0356
Russian Federation, Moscow
Larisa D. Maltseva
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Email: lamapost@mail.ru
ORCID iD: 0000-0002-4380-4522
SPIN-code: 7725-2499
MD, Cand. Sci. (Med.)
Russian Federation, MoscowOlga L. Morozova
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Email: morozova_ol@list.ru
ORCID iD: 0000-0003-2453-1319
SPIN-code: 1567-4113
MD, Dr. Sci. (Med.)
Russian Federation, MoscowReferences
- Patti GJ, Yanes O, Siuzdak G. Metabolomics: the apogee of the omictriology. Nat Rev Mol Cell Biol. 2012;13(4):263–269. doi: 10.1038/nrm3314
- Wishart DS. Metabolomics for investigating physiological and pathophysiological processes. Physiol Rev. 2019;99(4):1819–1875. doi: 10.1152/physrev.00035.2018
- Abbiss H, Maker GL, Trengove RD. Metabolomics approaches for the diagnosis and understanding of kidney diseases. Metabolites. 2019;9(2):34–55. doi: 10.3390/metabo9020034
- Khamis MM, Adamko DJ, El-Aneed A. Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrom Rev. 2017;36(2):115–134. doi: 10.1002/mas.21455
- Zhang A, Sun H, Wu X, Wang X. Urine metabolomics. Clin Chim Acta. 2012;414:65–69. doi: 10.1016/j.cca.2012.08.016
- Chiu C-Y, Yeh K-W, Lin G, et al. Metabolomics reveals dynamic metabolic changes associated with age in early childhood. PLoS One. 2016;11(2):e0149823. doi: 10.1371/journal.pone.0149823
- Stankovic AK, DiLauri E. Quality improvements in the preanalytical phase: Focus on urine specimen workflow. Clin Lab Med. 2008;28(2):339–350. doi: 10.1016/j.cll.2007.12.011
- Wang X, Gu H, Palma-Duran SA, et al. Influence of storage conditions and preservatives on metabolite fingerprints in urine. Metabolites. 2019;9(10):203–215. doi: 10.3390/metabo9100203
- Delanghe J, Speeckaert M. Preanalytical requirements of urinalysis. Biochem Med (Zagreb). 2014;24(1):89–104. doi: 10.11613/BM.2014.011
- Rodríguez-Morató J, Pozo ÓJ, Marcos J. Targeting human urinary metabolome by LC-MS/MS: a review. Bioanalysis. 2018;10(7):489–516. doi: 10.4155/bio-2017-0285
- Dunn WB, Broadhurst D, Ellis DI, et al. A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. Int J Epidemiol. 2008;37(S1):i23–i30. doi: 10.1093/ije/dym281
- Laparre J, Kaabia Z, Mooney M, et al. Impact of storage conditions on the urinary metabolomics fingerprint. Anal Chim Acta. 2017;951:99–107. doi: 10.1016/j.aca.2016.11.055
- Chaleckis R, Meister I, Zhang P, Wheelock CE. Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics. Curr Opin Biotechnol. 2019;55:44–50. doi: 10.1016/j.copbio.2018.07.010
- Bartel J, Krumsiek J, Theis FJ. Statistical methods for the analysis of high-throughput metabolomics data. Comput Struct Biotechnol J. 2013;4(5):e201301009. doi: 10.5936/csbj.201301009
- Wishart DS, Knox C, Guo AC, et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 2009;37(S1):D603–D610. doi: 10.1093/nar/gkn810
- Smith CA, O’Maille G, Want EJ, et al. METLIN: a metabolite mass spectral database. Ther Drug Monit. 2005;27(6):747–751. doi: 10.1097/01.ftd.0000179845.53213.39
- Bouatra S, Aziat F, Mandal R, et al. The human urine metabolome. PLoS One. 2013;8(9):e73076. doi: 10.1371/journal.pone.0073076
- Dai X, Shen L. Advances and trends in omics technology development. Front Med (Lausanne). 2022;9:911861. doi: 10.3389/fmed.2022.911861
- Bukharina AB, Fedulkina AO, Demidova KN, et al. Omics technologies in screening for kidney disease in children with congenital uropathy. Annals of the Russian academy of medical sciences. 2022;77(5):354–361. (In Russ.) doi: 10.15690/vramn2107
- Emwas A-H, Roy R, McKay RT, et al. NMR spectroscopy for metabolomics research. Metabolites. 2019;9(7):123. doi: 10.3390/metabo9070123
- Thomas SN, French D, Jannetto PJ, et al. Liquid chromatography–tandem mass spectrometry for clinical diagnostics. Nat Rev Methods Primers. 2022;2(1):96. doi: 10.1038/s43586-022-00175-x
- Fiehn O. Metabolomics by gas chromatography-mass spectrometry: combined targeted and untargeted profiling. Curr Protoc Mol Biol. 2016;114(1):30.4.1–30.4.32. doi: 10.1002/0471142727.mb3004s114
- Jokiniitty E, Hokkinen L, Kumpulainen P, et al. Urine headspace analysis with field asymmetric ion mobility spectrometry for detection of chronic kidney disease. Biomark Med. 2020;14(8):629–638. doi: 10.2217/bmm-2020-0085
- Fitzgerald J, Fenniri H. Cutting edge methods for non-invasive disease diagnosis using E-tongue and E-nose devices. Biosensors (Basel). 2017;7(4):59. doi: 10.3390/bios7040059
- Teruya T, Goga H, Yanagida M. Aging markers in human urine: A comprehensive, non-targeted LC-MS study. FASEB Bioadv. 2020;2(12):720–733. doi: 10.1096/fba.2020-00047
- Scalabre A, Jobard E, Demède D, et al. Evolution of newborns’ urinary metabolomic profiles according to age and growth. J Proteome Res. 2017;16(10):3732–3740. doi: 10.1021/acs.jproteome.7b00421
- Liu X, Tian X, Qinghong S, et al. Characterization of LC-MS based urine metabolomics in healthy children and adults. Peer J. 2022;10:e13545. doi: 10.7717/peerj.13545
- Reusch JEB, Kumar TR, Regensteiner JG, Zeitler PS. Conference participants. Identifying the critical gaps in research on sex differences in metabolism across the life span. Endocrinology. 2018;159(1):9–19. doi: 10.1210/en.2017-03019
- Tang WHW, Wang Z, Levison BS, et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med. 2013;368(17):1575–1584. doi: 10.1056/NEJMoa1109400
- Koeth RA, Wang Z, Levison BS, et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19(5):576–585. doi: 10.1038/nm.3145.
- Wilson Tang WH, Wang Z, Kennedy DJ, et al. Gut microbiota-dependent trimethylamine N-oxide (TMAO) pathway contributes to both development of renal insufficiency and mortality risk in chronic kidney disease. Circ Res. 2015;116(3):448–455. doi: 10.1161/CIRCRESAHA.116.305360
- Liu X, Yin P, Shao Y, et al. Which is the urine sample material of choice for metabolomics-driven biomarker studies? Anal Chim Acta. 2020;1105:120–127. doi: 10.1016/j.aca.2020.01.028
- Liu Z, Xia B, Saric J, et al. Effects of vancomycin and ciprofloxacin on the NMRI mouse metabolism. J Proteome Res. 2018;17(10):3565–3573. doi: 10.1021/acs.jproteome.8b00583
- Kim AHJ, Lee Y, Kim E, et al. Assessment of oral vancomycin-induced alterations in gut bacterial microbiota and metabolome of healthy men. Front Cell Infect Microbiol. 2021;11:629438. doi: 10.3389/fcimb.2021.629438
- Rodriguez MM. Congenital anomalies of the kidney and the urinary tract (CAKUT). Fetal PediatrPathol. 2014;33(5-6):293–320. doi: 10.3109/15513815.2014.959678
- Macioszek S, Wawrzyniak R, Kranz A, et al. Comprehensive metabolic signature of renal dysplasia in children. A multiplatform metabolomics concept. Front Mol Biosci. 2021;8:665661. doi: 10.3389/fmolb.2021.665661
- Xu X, Nie S, Zhang A, et al. Acute kidney injury among hospitalized children in China. Clin J Am Soc Nephrol. 2018;13(12):1791–1800. doi: 10.2215/CJN.00800118
- Sutherland SM, Ji J, Sheikhi FH, et al. AKI in hospitalized children: Epidemiology and clinical associations in a national cohort. Clin J Am Soc Nephrol. 2013;8(10):1661–1669. doi: 10.2215/CJN.00270113
- Andreoli SP. Acute kidney injury in children. Pediatr Nephrol. 2009;24(2):253–263. doi: 10.1007/s00467-008-1074-9
- Cleto-Yamane TL, Gomes CLR, Suassuna JHR, Nogueira PK. Acute kidney injury epidemiology in pediatrics. J Bras Nefrol. 2019;41(2):275–283. doi: 10.1590/2175-8239-JBN-2018-0127
- Mammen C, Abbas AA, Skippen P, et al. Long-term risk of CKD in children surviving episodes of acute kidney injury in the intensive care unit: A prospective cohort study. Am J Kidney Dis. 2012;59(4):523–530. doi: 10.1053/j.ajkd.2011.10.048
- Beger RD, Holland RD, Sun J, et al. Metabonomics of acute kidney injury in children after cardiac surgery. Pediatr Nephrol. 2008;23(6):977–984. doi: 10.1007/s00467-008-0756-7
- Muhle-Goll C, Eisenmann P, Luy B, et al. Urinary NMR profiling in pediatric acute kidney injury — a pilot study. Int J Mol Sci. 2020;21(4):1187. doi: 10.3390/ijms21041187
- Viau A, El Karoui K, Laouari D, et al. Lipocalin 2 is essential for chronic kidney disease progression in mice and humans. J Clin Invest. 2010;120(11):4065–4076. doi: 10.1172/JCI42004
- Harambat J, van Stralen KJ, Kim JJ, Tizard EJ. Epidemiology of chronic kidney disease in children. Pediatr Nephrol. 2012;27(3):363–373. doi: 10.1007/s00467-011-1939-1
- Becherucci F, Roperto RM, Materassi M, Romagnani P. Chronic kidney disease in children. Clin Kidney J. 2016;9(4):583–591. doi: 10.1093/ckj/sfw047
- Vivante A, Hildebrandt F. Exploring the genetic basis of early-onset chronic kidney disease. Nat Rev Nephrol. 2016;12(3):133–146. doi: 10.1038/nrneph.2015.205
- Martens-Lobenhoffer J, Bode-Böger SM. Amino acid N-acetylation: Metabolic elimination of symmetric dimethylarginine as symmetric Nα-acetyldimethylarginine, determined in human plasma and urine by LC–MS/MS. J Chromatogr B Analyt Technol Biomed Life Sci. 2015;975:59–64. doi: 10.1016/j.jchromb.2014.11.009
- Benito S, Sánchez A, Unceta N, et al. LC-QTOF-MS-based targeted metabolomics of arginine-creatine metabolic pathway-related compounds in plasma: application to identify potential biomarkers in pediatric chronic kidney disease. Anal Bioanal Chem. 2016;408(3):747–760. doi: 10.1007/s00216-015-9153-9
- Zhang W, Miikeda A, Zuckerman J, et al. Inhibition of microbiota-dependent TMAO production attenuates chronic kidney disease in mice. Sci Rep. 2021;11(10):518. doi: 10.1038/s41598-020-80063-0
- Wang Y-N, Ma S-X, Chen Y-Y, et al. Chronic kidney disease: Biomarker diagnosis to therapeutic targets. Clin Chim Acta. 2019;499:54–63. doi: 10.1016/j.cca.2019.08.030
- Mattoo TK. Vesicoureteral reflux and reflux nephropathy, advances in chronic kidney disease. Adv Chronic Kidney Dis. 2011;18(5):348–354. doi: 10.1053/j.ackd.2011.07.006
- Läckgren G, Cooper CS, Neveus T, Kirsch AJ. Management of vesicoureteral reflux: What have we learned over the last 20 years? Front Pediatr. 2021;9:650326. doi: 10.3389/fped.2021.650326
- Vitko D, McQuaid JW, Gheinani AH, et al. Urinary tract infections in children with vesicoureteral reflux are accompanied by alterations in urinary microbiota and metabolome profiles. Eur Urol. 2022;81(2):151–154. doi: 10.1016/j.eururo.2021.08.022
- Riccio S, Valentino MS, Passaro AP, et al. New insights from metabolomics in pediatric renal diseases. Children (Basel). 2022;9(1):118. doi: 10.3390/children9010118
- Morozova O, Morozov D, Pervouchine D, et al. Urinary biomarkers of latent inflammation and fibrosis in children with vesicoureteral reflux. Int Urol Nephrol. 2020;52(4):603–610. doi: 10.1007/s11255-019-02357-1