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Untargeted urinary metabolomic profiling in post-kidney transplant with different levels of kidney function

Yıl 2023, Cilt: 27 Sayı: 4, 1673 - 1686, 28.06.2025

Ihsan Yozgat Betul Sahin Neslihan Yildirim Saral Zafer Banu Ulusoy Meltem Kilercik Huseyin Celik Mahmut Esat Danışoglu Soner Duman , Bulent Oktay Mustafa Serteser Ahmet Tarık Baykal

Öz

The ability to monitor patients plays a major role in the success of kidney transplants. However, transplant monitoring still depends on relatively outdated, inadequate technologies. The aim of this study was to reveal the metabolomic profile of the kidney allograft using the metabolomic screening technique and to identify specific eGFR-based biomarkers to monitor individuals with different levels of post-transplantation graft dysfunction. In the current study, urine samples from 131 unique kidney transplant recipients were collected and analyzed by ultra-high performance liquid chromatography and benchtop QTof mass spectrometer (Xevo G2 XS QTof). Acquired data were first pre-processed by Progenesis QI 2.3 (Nonlinear Dynamics, Waters, UK). Putative annotation was performed against the HMDB database following multivariate statistical analysis. Post-transplant biomarker panels that can distinguish stages of renal dysfunction were created by combining the significant markers and taking their ratios. Overall, 8 metabolites were significantly altered within three groups of kidney transplant recipients:4,5-Dihydroorotic acid, N2-Succinyl-L-glutamic acid 5-semialdehyde, Valyl-Arginine, Pantothenic acid, L-phenylalanyl-L-hydroxyproline, MG(0:0/24:0/0:0), QYNAD and 12-Hydroxy-13-O-D-glucuronoside-octadec-9Z-enoate as biomarker candidates (p<0.05). The ratio of 4,5-Dihydroorotic acid to Pantothenic acid (panel-1) can be used to monitor kidney function. Specifically, these metabolite ratios were found to be more sensitive to changes in kidney function than panel-2, which consisted of 7 metabolites, excluding QYNAD, of the 8 major metabolites. Our results may contribute to the monitoring of kidney transplant patients based on post-transplant eGFR-based kidney function stages, thus providing a method for the early evaluation and monitoring of the kidney transplant recipient after transplantation for kidney transplant patient management.

Anahtar Kelimeler

Urine Metabolites metabolome profiling metabolomics UPLC/ESI/QTOF-MS/MS Kidney Transplantation

Kaynakça

  • [1] Cheung AK, Chang TI, Cushman WC, Furth SL, Hou FF, Ix JH, Knoll GA, Muntner P, Pecoits-Filho R, Sarnak MJ, Tobe SW, Tomson CRV, Mann JFE. KDIGO 2021 Clinical Practice Guideline for the Management of Blood Pressure in Chronic Kidney Disease. Kidney Int. 2021;99(3):S1-S87. https://doi.org/10.1016/j.kint.2020.11.003
  • [2] Jha V, Wang AYM, Wang H. The impact of CKD identification in large countries: the burden of illness. Nephrol Dial Transplant. 2012;27(suppl 3):iii32-iii38. https://doi.org/10.1093/ndt/gfs113
  • [3] Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F, Zhang YL, Coresh J, Levey AS. Estimating Glomerular Filtration Rate from Serum Creatinine and Cystatin C. N Engl J Med. 2012;367(1):20-29. https://doi.org/10.1056/nejmoa1114248
  • [4] Barrios C, Spector TD, Menni C. Blood, urine and faecal metabolite profiles in the study of adult renal disease. Arch Biochem Biophys. 2016;589:81-92. https://doi.org/10.1016/j.abb.2015.10.006
  • [5] Wong G, Hayen A, Chapman JR, Webster AC, Wang JJ, Mitchell P, Craig JC. Association of CKD and Cancer Risk in Older People. J Am Soc Nephrol. 2009;20(6):1341-1350. https://doi.org/10.1681/ASN.2008090998
  • [6] Liyanage T, Ninomiya T, Jha V, Neal B, Patrice HM, Okpechi I, Zhao MH, Lv J, Garg AX, Knight J, Rodgers A, Gallagher M, Kotwal S, Cass A, Perkovic V. Worldwide access to treatment for end-stage kidney disease: A systematic review. Lancet. 2015;385(9981):1975-1982. https://doi.org/10.1016/S0140-6736(14)61601-9
  • [7] Zhao YY. Metabolomics in chronic kidney disease. Clin Chim Acta. 2013;422:59-69. https://doi.org/10.1016/j.cca.2013.03.033
  • [8] Urbschat A, Obermüller N, Haferkamp A. Biomarkers of kidney injury. Biomarkers. 2011;16(SUPPL. 1):21-30. https://doi.org/10.3109/1354750X.2011.587129
  • [9] Solomon R, Goldstein S. Real-Time measurement of glomerular filtration rate. Curr Opin Crit Care. 2017;23(6):470-474. https://doi.org/10.1097/MCC.0000000000000456
  • [10] Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. https://doi.org/10.1186/s13059-017-1215-1
  • [11] Puchades-Carrasco L, Pineda-Lucena A. Metabolomics in pharmaceutical research and development. Curr Opin Biotechnol. 2015;35. https://doi.org/10.1016/j.copbio.2015.04.004
  • [12] Kapoore RV, Vaidyanathan S. Towards quantitative mass spectrometry-based metabolomics in microbial and mammalian systems. Philos Trans R Soc A Math Phys Eng Sci. 2016;374(2079). https://doi.org/10.1098/rsta.2015.0363
  • [13] Altaf-Ul-Amin M, Kanaya S, Mohamed-Hussein ZA. Investigating metabolic pathways and networks. Encycl Bioinforma Comput Biol ABC Bioinforma. 2018;1-3:489-503. https://doi.org/10.1016/B978-0-12-809633-8.20140-4
  • [14] Bedair M, Sumner LW. Current and emerging mass-spectrometry technologies for metabolomics. TrAC Trends Anal Chem. 2008;27(3). https://doi.org/10.1016/j.trac.2008.01.006
  • [15] Xiao L, Wang C, Dai C, Littlepage LE, Li J, Schultz ZD. Untargeted Tumor Metabolomics with Liquid Chromatography–Surface‐Enhanced Raman Spectroscopy. Angew Chemie Int Ed. 2020;59(9). https://doi.org/10.1002/anie.201912387
  • [16] Gagnebin Y, Julien B, Belén P, Serge R. Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage. J Pharm Biomed Anal. 2018;161. https://doi.org/10.1016/j.jpba.2018.08.046
  • [17] Worley B, Powers R. Multivariate Analysis in Metabolomics. Curr Metabolomics. 2013;1(1):92-107. https://doi.org/10.2174/2213235x11301010092
  • [18] Stoessel D, Stellmann JP, Willing A, Behrens B, Rosenkranz SC, Hodecker SC, Stürner KH, Reinhardt S, Fleischer S, Deuschle C, Maetzler W, Berg D, Heesen C, Walther D, Schauer N, Friese MA, Pless O. Metabolomic Profiles for Primary Progressive Multiple Sclerosis Stratification and Disease Course Monitoring. Front Hum Neurosci. 2018;12(June):1-13. https://doi.org/10.3389/fnhum.2018.00226
  • [19] Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315-1316. https://doi.org/10.1097/JTO.0b013e3181ec173d
  • [20] Bassi R, Niewczas MA, Biancone L, … Fiorina P. Metabolomic profiling in individuals with a failing kidney allograft. PLoS One. 2017;12(1):1-14. https://doi.org/10.1371/journal.pone.0169077
  • [21] Stenlund H, Madsen R, Vivi A, Calderisi M, Lundstedt T, Tassini M, Carmellini M, Trygg J. Monitoring kidney-transplant patients using metabolomics and dynamic modeling. Chemom Intell Lab Syst. 2009;98(1):45-50. https://doi.org/10.1016/j.chemolab.2009.04.013
  • [22] Stanimirova I, Banasik M, Ząbek A, Dawiskiba T, Kościelska-Kasprzak K, Wojtowicz W, Krajewska M, Janczak D, Młynarz P. Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation. Sci Rep. 2020;10(1):1-14. https://doi.org/10.1038/s41598-020-74245-z
  • [23] Sigdel TK, Schroeder AW, Yang JYC, Sarwal RD, Liberto JM, Sarwal MM. Targeted urine metabolomics for monitoring renal allograft injury and immunosuppression in pediatric patients. J Clin Med. 2020;9(8):1-14. https://doi.org/10.3390/jcm9082341
  • [24] Gooding J, Cao L, Whitaker C, Mwiza JM, Fernander M, Ahmed F, Acuff Z, McRitchie S, Sumner S, Ongeri EM. Meprin β metalloproteases associated with differential metabolite profiles in the plasma and urine of mice with type 1 diabetes and diabetic nephropathy. BMC Nephrol. 2019;20(1). https://doi.org/10.1186/s12882-019-1313-2
  • [25] Ceri NG, Gulle K, Arasli M, Akpolat M, Demirci B. Protective Effect of Vitamin B5 (Dexpanthenol) on Nephropathy in Streptozotocin Diabetic Rats. Meandros Med Dent J. 2021;22(1):53-56. https://doi.org/10.4274/meandros.galenos.2021.65002
  • [26] Ma T, Liu T, Xie P, Jiang S, Yi W, Dai P, Guo X. UPLC-MS-based urine nontargeted metabolic profiling identifies dysregulation of pantothenate and CoA biosynthesis pathway in diabetic kidney disease. Life Sci. 2020;258(6):118160. https://doi.org/10.1016/j.lfs.2020.118160
  • [27] Gao Z, Chen X. Fatty Acid β-Oxidation in Kidney Diseases: Perspectives on Pathophysiological Mechanisms and Therapeutic Opportunities. Front Pharmacol. 2022;13(April):1-10. https://doi.org/10.3389/fphar.2022.805281
  • [28] Gibson KM, Nyhan WL. Metabolism of [U-14C]-4-hydroxybutyric acid to intermediates of the tricarboxylic acid cycle in extracts of rat liver and kidney mitochondria. Eur J Drug Metab Pharmacokinet. 1989;14(1):61-70. https://doi.org/10.1007/BF03190843
  • [29] Dohkrty JD, Stout RW, Roth RH. Metabolism of [1-14C]γ-hydroxybutyric acid by rat brain after intraventricular injection. Biochem Pharmacol. 1975;24(4):469-474. https://doi.org/10.1016/0006-2952(75)90130-6
  • [30] Doherty JD, Roth RH. Metabolism of Γ‐Hydroxy‐[1‐14C] Butyrate By Rat Brain: Relationship To the Krebs Cycle and Metabolic Compartmentation of Amino Acids. J Neurochem. 1978;30(6):1305-1309. https://doi.org/10.1111/j.1471-4159.1978.tb10460.x
  • [31] Chambliss KL, Lee CF, Ogier H, Rabier D, Jakobs C, Gibson KM. Enzymatic and immunological demonstration of normal and defective succinic semialdehyde dehydrogenase activity in fetal brain, liver and kidney. J Inherit Metab Dis. 1993;16(3):523-526. https://doi.org/10.1007/BF00711671
  • [32] Wang M, Xia W, Liu H, Liu F, Li H, Chang H, Sun J, Liu W, Sun X, Jiang Y, Liu H, Wu C, Pan X, Li Y, Rang W, Lu S, Xu S. Urinary metabolomics reveals novel interactions between metal exposure and amino acid metabolic stress during pregnancy. Toxicol Res (Camb). 2018;7(6):1164-1172. https://doi.org/10.1039/C8TX00042E
  • [33] Popolo A, Adesso S, Pinto A, Autore G, Marzocco S. L-Arginine and its metabolites in kidney and cardiovascular disease. Amino Acids. 2014;46(10):2271-2286. https://doi.org/10.1007/s00726-014-1825-9
  • [34] Yin F, Ling Y, Martin J, Narayanaswamy R, McIntosh L, Li F, Liu G. Quantitation of uridine and L-dihydroorotic acid in human plasma by LC–MS/MS using a surrogate matrix approach. J Pharm Biomed Anal. 2021;192:113669. https://doi.org/10.1016/j.jpba.2020.113669
  • [35] Mazumder MK, Phukan BC, Bhattacharjee A, Borah A. Disturbed purine nucleotide metabolism in chronic kidney disease is a risk factor for cognitive impairment. Med Hypotheses. 2018;111:36-39. https://doi.org/10.1016/j.mehy.2017.12.016
  • [36] Zhang ZH, Vaziri ND, Wei F, Cheng XL, Bai X, Zhao YY. An integrated lipidomics and metabolomics reveal nephroprotective effect and biochemical mechanism of Rheum officinale in chronic renal failure. Sci Rep. 2016;6(2):1-18. https://doi.org/10.1038/srep22151
  • [37] Hocher B, Adamski J. Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol. 2017;13(5):269-284. https://doi.org/10.1038/nrneph.2017.30
  • [38] Magalhães P, Pejchinovski M, Markoska K, … Schanstra JP. Association of kidney fibrosis with urinary peptides: A path towards non-invasive liquid biopsies? Sci Rep. 2017;7(1):1-8. https://doi.org/10.1038/s41598-017-17083-w
  • [39] Mavrogeorgis E, Mischak H, Latosinska A, Vlahou A, Schanstra JP, Siwy J, Jankowski V, Beige J, Jankowski J. Collagen-derived peptides in CKD: A link to fibrosis. Toxins (Basel). 2022;14(1):1-13. https://doi.org/10.3390/toxins14010010
  • [40] Villanueva J, Shaffer DR, Philip J, Chaparro CA, Erdjument-Bromage H, Olshen AB, Fleisher M, Lilja H, Brogi E, Boyd J, Sanchez-Carbayo M, Holland EC, Cordon-Cardo C, Scher HI, Tempst P. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J Clin Invest. 2006;116(1):271-284. https://doi.org/10.1172/JCI26022
  • [41] 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;829:40-47. https://doi.org/10.1016/j.aca.2014.04.040
  • [42] Weber F, Rüdel R, Aulkemeyer P, Brinkmeier H. The endogenous pentapeptide QYNAD induces acute conduction block in the isolated rat sciatic nerve. Neurosci Lett. 2002;317(1):33-36. https://doi.org/10.1016/S0304-3940(01)02420-X
  • [43] Gowda GAN, Zhang S, Gu H, Asiago V, Shanaiah N, Raftery D. Metabolomics-based methods for early disease diagnostics. Expert Rev Mol Diagn. 2008;8(5):617-633. https://doi.org/10.1586/14737159.8.5.617

Yıl 2023, Cilt: 27 Sayı: 4, 1673 - 1686, 28.06.2025

Ihsan Yozgat Betul Sahin Neslihan Yildirim Saral Zafer Banu Ulusoy Meltem Kilercik Huseyin Celik Mahmut Esat Danışoglu Soner Duman , Bulent Oktay Mustafa Serteser Ahmet Tarık Baykal

Öz

Kaynakça

  • [1] Cheung AK, Chang TI, Cushman WC, Furth SL, Hou FF, Ix JH, Knoll GA, Muntner P, Pecoits-Filho R, Sarnak MJ, Tobe SW, Tomson CRV, Mann JFE. KDIGO 2021 Clinical Practice Guideline for the Management of Blood Pressure in Chronic Kidney Disease. Kidney Int. 2021;99(3):S1-S87. https://doi.org/10.1016/j.kint.2020.11.003
  • [2] Jha V, Wang AYM, Wang H. The impact of CKD identification in large countries: the burden of illness. Nephrol Dial Transplant. 2012;27(suppl 3):iii32-iii38. https://doi.org/10.1093/ndt/gfs113
  • [3] Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F, Zhang YL, Coresh J, Levey AS. Estimating Glomerular Filtration Rate from Serum Creatinine and Cystatin C. N Engl J Med. 2012;367(1):20-29. https://doi.org/10.1056/nejmoa1114248
  • [4] Barrios C, Spector TD, Menni C. Blood, urine and faecal metabolite profiles in the study of adult renal disease. Arch Biochem Biophys. 2016;589:81-92. https://doi.org/10.1016/j.abb.2015.10.006
  • [5] Wong G, Hayen A, Chapman JR, Webster AC, Wang JJ, Mitchell P, Craig JC. Association of CKD and Cancer Risk in Older People. J Am Soc Nephrol. 2009;20(6):1341-1350. https://doi.org/10.1681/ASN.2008090998
  • [6] Liyanage T, Ninomiya T, Jha V, Neal B, Patrice HM, Okpechi I, Zhao MH, Lv J, Garg AX, Knight J, Rodgers A, Gallagher M, Kotwal S, Cass A, Perkovic V. Worldwide access to treatment for end-stage kidney disease: A systematic review. Lancet. 2015;385(9981):1975-1982. https://doi.org/10.1016/S0140-6736(14)61601-9
  • [7] Zhao YY. Metabolomics in chronic kidney disease. Clin Chim Acta. 2013;422:59-69. https://doi.org/10.1016/j.cca.2013.03.033
  • [8] Urbschat A, Obermüller N, Haferkamp A. Biomarkers of kidney injury. Biomarkers. 2011;16(SUPPL. 1):21-30. https://doi.org/10.3109/1354750X.2011.587129
  • [9] Solomon R, Goldstein S. Real-Time measurement of glomerular filtration rate. Curr Opin Crit Care. 2017;23(6):470-474. https://doi.org/10.1097/MCC.0000000000000456
  • [10] Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. https://doi.org/10.1186/s13059-017-1215-1
  • [11] Puchades-Carrasco L, Pineda-Lucena A. Metabolomics in pharmaceutical research and development. Curr Opin Biotechnol. 2015;35. https://doi.org/10.1016/j.copbio.2015.04.004
  • [12] Kapoore RV, Vaidyanathan S. Towards quantitative mass spectrometry-based metabolomics in microbial and mammalian systems. Philos Trans R Soc A Math Phys Eng Sci. 2016;374(2079). https://doi.org/10.1098/rsta.2015.0363
  • [13] Altaf-Ul-Amin M, Kanaya S, Mohamed-Hussein ZA. Investigating metabolic pathways and networks. Encycl Bioinforma Comput Biol ABC Bioinforma. 2018;1-3:489-503. https://doi.org/10.1016/B978-0-12-809633-8.20140-4
  • [14] Bedair M, Sumner LW. Current and emerging mass-spectrometry technologies for metabolomics. TrAC Trends Anal Chem. 2008;27(3). https://doi.org/10.1016/j.trac.2008.01.006
  • [15] Xiao L, Wang C, Dai C, Littlepage LE, Li J, Schultz ZD. Untargeted Tumor Metabolomics with Liquid Chromatography–Surface‐Enhanced Raman Spectroscopy. Angew Chemie Int Ed. 2020;59(9). https://doi.org/10.1002/anie.201912387
  • [16] Gagnebin Y, Julien B, Belén P, Serge R. Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage. J Pharm Biomed Anal. 2018;161. https://doi.org/10.1016/j.jpba.2018.08.046
  • [17] Worley B, Powers R. Multivariate Analysis in Metabolomics. Curr Metabolomics. 2013;1(1):92-107. https://doi.org/10.2174/2213235x11301010092
  • [18] Stoessel D, Stellmann JP, Willing A, Behrens B, Rosenkranz SC, Hodecker SC, Stürner KH, Reinhardt S, Fleischer S, Deuschle C, Maetzler W, Berg D, Heesen C, Walther D, Schauer N, Friese MA, Pless O. Metabolomic Profiles for Primary Progressive Multiple Sclerosis Stratification and Disease Course Monitoring. Front Hum Neurosci. 2018;12(June):1-13. https://doi.org/10.3389/fnhum.2018.00226
  • [19] Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315-1316. https://doi.org/10.1097/JTO.0b013e3181ec173d
  • [20] Bassi R, Niewczas MA, Biancone L, … Fiorina P. Metabolomic profiling in individuals with a failing kidney allograft. PLoS One. 2017;12(1):1-14. https://doi.org/10.1371/journal.pone.0169077
  • [21] Stenlund H, Madsen R, Vivi A, Calderisi M, Lundstedt T, Tassini M, Carmellini M, Trygg J. Monitoring kidney-transplant patients using metabolomics and dynamic modeling. Chemom Intell Lab Syst. 2009;98(1):45-50. https://doi.org/10.1016/j.chemolab.2009.04.013
  • [22] Stanimirova I, Banasik M, Ząbek A, Dawiskiba T, Kościelska-Kasprzak K, Wojtowicz W, Krajewska M, Janczak D, Młynarz P. Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation. Sci Rep. 2020;10(1):1-14. https://doi.org/10.1038/s41598-020-74245-z
  • [23] Sigdel TK, Schroeder AW, Yang JYC, Sarwal RD, Liberto JM, Sarwal MM. Targeted urine metabolomics for monitoring renal allograft injury and immunosuppression in pediatric patients. J Clin Med. 2020;9(8):1-14. https://doi.org/10.3390/jcm9082341
  • [24] Gooding J, Cao L, Whitaker C, Mwiza JM, Fernander M, Ahmed F, Acuff Z, McRitchie S, Sumner S, Ongeri EM. Meprin β metalloproteases associated with differential metabolite profiles in the plasma and urine of mice with type 1 diabetes and diabetic nephropathy. BMC Nephrol. 2019;20(1). https://doi.org/10.1186/s12882-019-1313-2
  • [25] Ceri NG, Gulle K, Arasli M, Akpolat M, Demirci B. Protective Effect of Vitamin B5 (Dexpanthenol) on Nephropathy in Streptozotocin Diabetic Rats. Meandros Med Dent J. 2021;22(1):53-56. https://doi.org/10.4274/meandros.galenos.2021.65002
  • [26] Ma T, Liu T, Xie P, Jiang S, Yi W, Dai P, Guo X. UPLC-MS-based urine nontargeted metabolic profiling identifies dysregulation of pantothenate and CoA biosynthesis pathway in diabetic kidney disease. Life Sci. 2020;258(6):118160. https://doi.org/10.1016/j.lfs.2020.118160
  • [27] Gao Z, Chen X. Fatty Acid β-Oxidation in Kidney Diseases: Perspectives on Pathophysiological Mechanisms and Therapeutic Opportunities. Front Pharmacol. 2022;13(April):1-10. https://doi.org/10.3389/fphar.2022.805281
  • [28] Gibson KM, Nyhan WL. Metabolism of [U-14C]-4-hydroxybutyric acid to intermediates of the tricarboxylic acid cycle in extracts of rat liver and kidney mitochondria. Eur J Drug Metab Pharmacokinet. 1989;14(1):61-70. https://doi.org/10.1007/BF03190843
  • [29] Dohkrty JD, Stout RW, Roth RH. Metabolism of [1-14C]γ-hydroxybutyric acid by rat brain after intraventricular injection. Biochem Pharmacol. 1975;24(4):469-474. https://doi.org/10.1016/0006-2952(75)90130-6
  • [30] Doherty JD, Roth RH. Metabolism of Γ‐Hydroxy‐[1‐14C] Butyrate By Rat Brain: Relationship To the Krebs Cycle and Metabolic Compartmentation of Amino Acids. J Neurochem. 1978;30(6):1305-1309. https://doi.org/10.1111/j.1471-4159.1978.tb10460.x
  • [31] Chambliss KL, Lee CF, Ogier H, Rabier D, Jakobs C, Gibson KM. Enzymatic and immunological demonstration of normal and defective succinic semialdehyde dehydrogenase activity in fetal brain, liver and kidney. J Inherit Metab Dis. 1993;16(3):523-526. https://doi.org/10.1007/BF00711671
  • [32] Wang M, Xia W, Liu H, Liu F, Li H, Chang H, Sun J, Liu W, Sun X, Jiang Y, Liu H, Wu C, Pan X, Li Y, Rang W, Lu S, Xu S. Urinary metabolomics reveals novel interactions between metal exposure and amino acid metabolic stress during pregnancy. Toxicol Res (Camb). 2018;7(6):1164-1172. https://doi.org/10.1039/C8TX00042E
  • [33] Popolo A, Adesso S, Pinto A, Autore G, Marzocco S. L-Arginine and its metabolites in kidney and cardiovascular disease. Amino Acids. 2014;46(10):2271-2286. https://doi.org/10.1007/s00726-014-1825-9
  • [34] Yin F, Ling Y, Martin J, Narayanaswamy R, McIntosh L, Li F, Liu G. Quantitation of uridine and L-dihydroorotic acid in human plasma by LC–MS/MS using a surrogate matrix approach. J Pharm Biomed Anal. 2021;192:113669. https://doi.org/10.1016/j.jpba.2020.113669
  • [35] Mazumder MK, Phukan BC, Bhattacharjee A, Borah A. Disturbed purine nucleotide metabolism in chronic kidney disease is a risk factor for cognitive impairment. Med Hypotheses. 2018;111:36-39. https://doi.org/10.1016/j.mehy.2017.12.016
  • [36] Zhang ZH, Vaziri ND, Wei F, Cheng XL, Bai X, Zhao YY. An integrated lipidomics and metabolomics reveal nephroprotective effect and biochemical mechanism of Rheum officinale in chronic renal failure. Sci Rep. 2016;6(2):1-18. https://doi.org/10.1038/srep22151
  • [37] Hocher B, Adamski J. Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol. 2017;13(5):269-284. https://doi.org/10.1038/nrneph.2017.30
  • [38] Magalhães P, Pejchinovski M, Markoska K, … Schanstra JP. Association of kidney fibrosis with urinary peptides: A path towards non-invasive liquid biopsies? Sci Rep. 2017;7(1):1-8. https://doi.org/10.1038/s41598-017-17083-w
  • [39] Mavrogeorgis E, Mischak H, Latosinska A, Vlahou A, Schanstra JP, Siwy J, Jankowski V, Beige J, Jankowski J. Collagen-derived peptides in CKD: A link to fibrosis. Toxins (Basel). 2022;14(1):1-13. https://doi.org/10.3390/toxins14010010
  • [40] Villanueva J, Shaffer DR, Philip J, Chaparro CA, Erdjument-Bromage H, Olshen AB, Fleisher M, Lilja H, Brogi E, Boyd J, Sanchez-Carbayo M, Holland EC, Cordon-Cardo C, Scher HI, Tempst P. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J Clin Invest. 2006;116(1):271-284. https://doi.org/10.1172/JCI26022
  • [41] 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;829:40-47. https://doi.org/10.1016/j.aca.2014.04.040
  • [42] Weber F, Rüdel R, Aulkemeyer P, Brinkmeier H. The endogenous pentapeptide QYNAD induces acute conduction block in the isolated rat sciatic nerve. Neurosci Lett. 2002;317(1):33-36. https://doi.org/10.1016/S0304-3940(01)02420-X
  • [43] Gowda GAN, Zhang S, Gu H, Asiago V, Shanaiah N, Raftery D. Metabolomics-based methods for early disease diagnostics. Expert Rev Mol Diagn. 2008;8(5):617-633. https://doi.org/10.1586/14737159.8.5.617
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Ihsan Yozgat 0000-0002-0065-4480

Betul Sahin 0000-0001-8663-5741

Neslihan Yildirim Saral 0000-0002-6091-5048

Zafer Banu Ulusoy 0000-0002-7736-1862

Meltem Kilercik 0000-0001-6837-8892

Huseyin Celik 0000-0002-7107-431X

Mahmut Esat Danışoglu 0000-0001-7212-2419

Soner Duman

Bulent Oktay 0000-0002-0065-0340

Mustafa Serteser 0000-0001-7868-7613

Ahmet Tarık Baykal 0000-0002-8814-7351

Yayımlanma Tarihi 28 Haziran 2025
Yayımlandığı Sayı Yıl 2023 Cilt: 27 Sayı: 4

Kaynak Göster

APA Yozgat, I., Sahin, B., Yildirim Saral, N., Ulusoy, Z. B., vd. (2025). Untargeted urinary metabolomic profiling in post-kidney transplant with different levels of kidney function. Journal of Research in Pharmacy, 27(4), 1673-1686.
AMA Yozgat I, Sahin B, Yildirim Saral N, Ulusoy ZB, Kilercik M, Celik H, Danışoglu ME, Duman S, Oktay B, Serteser M, Baykal AT. Untargeted urinary metabolomic profiling in post-kidney transplant with different levels of kidney function. J. Res. Pharm. Temmuz 2025;27(4):1673-1686.
Chicago Yozgat, Ihsan, Betul Sahin, Neslihan Yildirim Saral, Zafer Banu Ulusoy, Meltem Kilercik, Huseyin Celik, Mahmut Esat Danışoglu, Soner Duman, Bulent Oktay, Mustafa Serteser, ve Ahmet Tarık Baykal. “Untargeted Urinary Metabolomic Profiling in Post-Kidney Transplant With Different Levels of Kidney Function”. Journal of Research in Pharmacy 27, sy. 4 (Temmuz 2025): 1673-86.
EndNote Yozgat I, Sahin B, Yildirim Saral N, Ulusoy ZB, Kilercik M, Celik H, Danışoglu ME, Duman S, Oktay B, Serteser M, Baykal AT (01 Temmuz 2025) Untargeted urinary metabolomic profiling in post-kidney transplant with different levels of kidney function. Journal of Research in Pharmacy 27 4 1673–1686.
IEEE I. Yozgat, “Untargeted urinary metabolomic profiling in post-kidney transplant with different levels of kidney function”, J. Res. Pharm., c. 27, sy. 4, ss. 1673–1686, 2025.
ISNAD Yozgat, Ihsan vd. “Untargeted Urinary Metabolomic Profiling in Post-Kidney Transplant With Different Levels of Kidney Function”. Journal of Research in Pharmacy 27/4 (Temmuz 2025), 1673-1686.
JAMA Yozgat I, Sahin B, Yildirim Saral N, Ulusoy ZB, Kilercik M, Celik H, Danışoglu ME, Duman S, Oktay B, Serteser M, Baykal AT. Untargeted urinary metabolomic profiling in post-kidney transplant with different levels of kidney function. J. Res. Pharm. 2025;27:1673–1686.
MLA Yozgat, Ihsan vd. “Untargeted Urinary Metabolomic Profiling in Post-Kidney Transplant With Different Levels of Kidney Function”. Journal of Research in Pharmacy, c. 27, sy. 4, 2025, ss. 1673-86.
Vancouver Yozgat I, Sahin B, Yildirim Saral N, Ulusoy ZB, Kilercik M, Celik H, Danışoglu ME, Duman S, Oktay B, Serteser M, Baykal AT. Untargeted urinary metabolomic profiling in post-kidney transplant with different levels of kidney function. J. Res. Pharm. 2025;27(4):1673-86.