Abstract
A typical characteristic of chronic kidney disease (CKD) is the progressive loss in renal function over a period of months or years with the concomitant accumulation of uremic retention solutes in the body. Known biomarkers for the kidney deterioration, such as serum creatinine or urinary albumin, do not allow effective early detection of CKD, which is essential towards disease management. In this work, a hydrophilic interaction liquid chromatography time-of-flight mass spectrometric (HILIC-TOF MS) platform was optimized allowing the search for novel uremic retention solutes and/or biomarkers of CKD. The HILIC-ESI-MS approach was used for the comparison of urine and plasma samples from CKD patients at stage 3 (n = 20), at stage 5 not yet receiving dialysis (n = 20) and from healthy controls (n = 20). Quality control samples were used to control and ensure the validity of the metabolomics approach. Subsequently the data were treated with the XCMS software for multivariate statistical analysis. In this way, differentiation could be achieved between the measured metabolite profile of the CKD patients versus the healthy controls. The approach allowed the elucidation of a number of metabolites that showed a significant up- and downregulation throughout the different stages of CKD. These compounds are cinnamoylglycine, glycoursodeoxycholic acid, 2-hydroxyethane sulfonate, and pregnenolone sulfate of which the identity was unambiguously confirmed via the use of authentic standards. The latter three are newly identified uremic retention solutes.
Similar content being viewed by others
References
Stenvinkel P. Chronic kidney disease: a public health priority and harbinger of premature cardiovascular disease. J Intern Med. 2010;268:456–67.
Vanholder R, De Smet R. Pathophysiologic effects of uremic retention solutes. J Am Soc Nephrol. 1999;10:1815–23.
Vanholder R, Baurmeister U, Brunet P, Cohen G, Glorieux G, Jankowski J. A bench to bedside view of uremic toxins. J Am Soc Nephrol. 2008;19:863–70.
Neirynck N, Vanholder R, Schepers E, Eloot S, Pletinck A, Glorieux G. An update on uremic toxins. Int Urol Nephrol. 2013;45:139–50.
Vanholder R, De Smet R, Glorieux G, Argilés A, Baurmeister U, Brunet P, et al. Review on uremic toxins: classification, concentration, and interindividual variability. Kidney Int. 2003;63:1934–43.
Duranton F, Cohen G, De Smet R, Rodriguez M, Jankowski J, Vanholder R, et al. Normal and pathologic concentrations of uremic toxins. J Am Soc Nephrol. 2012;23:1258–70.
Vassalotti JA, Stevens LA, Levey AS. Testing for chronic kidney disease: a position statement from the National Kidney Foundation. Am J Kidney Dis. 2007;50:169–80.
Nickolas TL, Barasch J, Devarajan P. Biomarkers in acute and chronic kidney disease. Curr Opin Nephrol Hypertens. 2008;17:127–32.
Wu I, Parikh CR. Screening for kidney diseases: older measures versus novel biomarkers. Clin J Am Soc Nephrol. 2008;3:1895–901.
Fassett RG, Venuthurupalli SK, Gobe GC, Coombes JS, Cooper MA, Hoy WE. Biomarkers in chronic kidney disease: a review. Kidney Int. 2011;80:806–21.
Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function—measured and estimated glomerular filtration rate. N Engl J Med. 2006;354:2473–83.
National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39:S1–266.
Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13:263–9.
Griffin JL, Shockcor JP. Metabolic profiles of cancer cells. Nat Rev Cancer. 2004;4:551–61.
Qi S, Ouyang X, Wang L, Peng W, Wen J, Dai Y. A pilot metabolic profiling study in serum of patients with chronic kidney disease based on 1H-NMR-spectroscopy. Clin Transl Sci. 2012;5:379–85.
Sui W, Li L, Che W, Guimai Z, Chen J, Li W, et al. A proton nuclear magnetic resonance-based metabonomics study of metabolic profiling in immunoglobulin a nephropathy. Clin. 2012;67:363–73.
Mutsaers HA, Engelke UF, Wilmer MJ, Wetzels JF, Wevers RA, van den Heuvel LP, et al. Optimized metabolomic approach to identify uremic solutes in plasma of stage 3–4 chronic kidney disease patients. PLoS One. 2013;8:e71199.
Tao X, Liu Y, Wang Y, Qiu Y, Lin J, Zhao A, et al. GC-MS with ethyl chloroformate derivatization for comprehensive analysis of metabolites in serum and its application to human uremia. Anal Bioanal Chem. 2008;391:2881–9.
Grabowska-Polanowska B, Faber J, Skowron M, Miarka P, Pietrzycka A, Sliwka I, et al. Detection of potential chronic kidney disease markers in breath using gas chromatography with mass-spectral detection coupled with thermal desorption method. J Chromatogr A. 2013;1301:179–89.
Pagonas N, Vautz W, Seifert L, Slodzinski R, Jankowski J, Zidek W, et al. Volatile organic compounds in uremia. PLoS One. 2012;7:e46258.
Rhee EP, Souza A, Farrell L, Pollak MR, Lewis GD, Steele DJR, et al. Metabolite profiling identifies markers of uremia. J Am Soc Nephrol. 2010;21:1041–51.
Jia L, Chen J, Yin P, Lu X, Xu G. Serum metabonomics study of chronic renal failure by ultra performance liquid chromatography coupled with Q-TOF mass spectrometry. Metab. 2008;4:183–9.
Aronov PA, Luo FJ, Plummer NS, Quan Z, Holmes S, Hostetter TH, et al. Colonic contribution to uremic solutes. J Am Soc Nephrol. 2011;22:1769–76.
Toyohara T, Akiyama Y, Suzuki T, Takeuchi Y, Mishima E, Tanemoto M, et al. Metabolomic profiling of uremic solutes in CKD patients. Hypertens Res. 2010;33:944–52.
Hayashi K, Sasamura H, Hishiki T, Suematsu M, Ikeda S, Soga T, et al. Use of serum and urine metabolome analysis for the detection of metabolic changes in patients with stage 1–2 chronic kidney disease. Nephro-Urol Mon. 2011;3:164–71.
Hirayama A, Nakashima E, Sugimoto M, Akiyama S, Sato W, Maruyama S, et al. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal Bioanal Chem. 2012;404:3101–9.
Posada-Ayala M, Zubiri I, Martin-Lorenzo M, Sanz-Maroto A, Molero D, Gonzalez-Calero L, et al. (2014) Identification of a urine metabolomic signature in patients with advanced-stage chronic kidney disease. Kidney Int 85(1):103–11.
Shah VO, Townsend RR, Feldman HI, Pappan KL, Kensicki E, Vander Jagt DL. Plasma metabolomic profiles in different stages of CKD. Clin J Am Soc Nephrol. 2013;8:363–70.
Boelaert J, t’Kindt R, Schepers E, Jorge L, Glorieux G, Neirynck N, et al. State-of-the-art non-targeted metabolomics in the study of chronic kidney disease. Metab. 2013;10:425–42.
Chen J, Wang W, Lv S, Yin P, Zhao X, Lu X, et al. Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. Anal Chim Acta. 2009;650:3–9.
Lin L, Huang Z, Gao Y, Yan X, Xing J, Hang W. LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery. J Proteome Res. 2010;10:1396–405.
Spagou K, Wilson ID, Masson P, Theodoridis G, Raikos N, Coen M, et al. HILIC-UPLC-MS for exploratory urinary metabolic profiling in toxicological studies. Anal Chem. 2011;83:382–90.
Chen J, Zhou L, Zhang X, Lu X, Cao R, Xu C, et al. Urinary hydrophilic and hydrophobic metabolic profiling based on liquid chromatography-mass spectrometry methods: differential metabolite discovery specific to ovarian cancer. Electrophor. 2012;33:3361–9.
Ivanisevic J, Zhu ZJ, Plate L, Tautenhahn R, Chen S, O’Brien PJ, et al. Toward ’omic scale metabolite profiling: a dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal Chem. 2013;85:6876–84.
Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006;78:779–87.
Warrack BM, Hnatyshyn S, Ott KH, Reily MD, Sanders M, Zhang H, et al. Normalization strategies for metabonomic analysis of urine samples. J Chromatogr B Analyt Technol Biomed Life Sci. 2009;877:547–52.
Tautenhahn R, Patti GJ, Kalisiak E, Miyamoto T, Schmidt M, Lo FY, et al. metaXCMS: second-order analysis of untargeted metabolomics data. Anal Chem. 2011;83:696–700.
Kuhl C, Tautenhahn R, Bottcher C, Larson TR, Neumann S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 2012;84:283–9.
Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabol. 2007;3:211–21.
Dawson PA, Lan T, Rao A. Bile acid transporters. J Lipid Res. 2009;50:2340–57.
Fernandes A, Vaz AR, Falcao AS, Silva RF, Brito MA, Brites D. Glycoursodeoxycholic acid and interleukin-10 modulate the reactivity of rat cortical astrocytes to unconjugated bilirubin. J Neuropathol Exp Neurol. 2007;66:789–98.
Vaz AR, Delgado-Esteban M, Brito MA, Bolanos JP, Brites D, Almeida A. Bilirubin selectively inhibits cytochrome c oxidase activity and induces apoptosis in immature cortical neurons: assessment of the protective effects of glycoursodeoxycholic acid. J Neurochem. 2010;112:56–65.
Brito MA, Lima S, Fernandes A, Falcao AS, Silva RF, Butterfield DA, et al. Bilirubin injury to neurons: contribution of oxidative stress and rescue by glycoursodeoxycholic acid. Neurotox. 2008;29:259–69.
Fellman JH, Roth ES, Avedovech NA, McCarthy KD. The metabolism of taurine to isethionate. Arch Biochem Biophys. 1980;204:560–7.
Snapper I, Yü TF, Chiang YT. Cinnamic acid metabolism in man. Exp Biol Med. 1940;44:30–4.
Sirich TL, Aronov PA, Plummer NS, Hostetter TH, Meyer TW. Numerous protein-bound solutes are cleared by the kidney with high efficiency. Kidney Int. 2013;84:585–90.
Harteneck C. Pregnenolone sulfate: from steroid metabolite to TRP channel ligand. Mol. 2013;18:12012–28.
St-Pierre MV, Hagenbuch B, Ugele B, Meier PJ, Stallmach T. Characterization of an organic anion-transporting polypeptide (OATP-B) in human placenta. J Clin Endocrinol Metab. 2002;87:1856–63.
Grube M, Kock K, Karner S, Reuther S, Ritter CA, Jedlitschky G, et al. Modification of OATP2B1-mediated transport by steroid hormones. Mol Pharmacol. 2006;70:1735–41.
Geyer J, Doring B, Meerkamp K, Ugele B, Bakhiya N, Fernandes CF, et al. Cloning and functional characterization of human sodium-dependent organic anion transporter (SLC10A6). J Biol Chem. 2007;282:19728–41.
Grosser G, Fietz D, Gunther S, Bakhaus K, Schweigmann H, Ugele B, et al. Cloning and functional characterization of the mouse sodium-dependent organic anion transporter Soat (Slc10a6). J Steroid Biochem Mol Biol. 2013;138:90–9.
Fang F, Christian WV, Gorman SG, Cui M, Huang J, Tieu K, et al. Neurosteroid transport by the organic solute transporter OSTalpha-OSTbeta. J Neurochem. 2010;115:220–33.
Majewska MD, Mienville JM, Vicini S. Neurosteroid pregnenolone sulfate antagonizes electrophysiological responses to GABA in neurons. Neurosci Lett. 1988;90:279–84.
Majewska MD, Demirgoren S, London ED. Binding of pregnenolone sulfate to rat brain membranes suggests multiple sites of steroid action at the GABAA receptor. Eur J Pharmacol. 1990;189:307–15.
Wu FS, Gibbs TT, Farb DH. Pregnenolone sulfate: a positive allosteric modulator at the N-methyl-D-aspartate receptor. Mol Pharmacol. 1991;40:333–6.
Wagner TF, Loch S, Lambert S, Straub I, Mannebach S, Mathar I, et al. Transient receptor potential M3 channels are ionotropic steroid receptors in pancreatic beta cells. Nat Cell Biol. 2008;10:1421–30.
Lambert S, Drews A, Rizun O, Wagner TF, Lis A, Mannebach S, et al. Transient receptor potential melastatin 1 (TRPM1) is an ion-conducting plasma membrane channel inhibited by zinc ions. J Biol Chem. 2011;286:12221–33.
Kullak-Ublick GA, Fisch T, Oswald M, Hagenbuch B, Meier PJ, Beuers U, et al. Dehydroepiandrosterone sulfate (DHEAS): identification of a carrier protein in human liver and brain. FEBS Lett. 1998;424:173–6.
Twede V, Tartaglia AL, Covey DF, Bamber BA. The neurosteroids dehydroepiandrosterone sulfate and pregnenolone sulfate inhibit the UNC-49 GABA receptor through a common set of residues. Mol Pharmacol. 2007;72:1322–9.
Monnet FP, Mahé V, Robel P, Baulieu EE. Neurosteroids, via sigma receptors, modulate the [3H]norepinephrine release evoked by N-methyl-D-aspartate in the rat hippocampus. Proc Natl Acad Sci. 1995;92:3774–8.
D’Hooge R, Van de Vijver G, Van Bogaert PP, Marescau B, Vanholder R, De Deyn PP. Involvement of voltage- and ligand-gated Ca2+ channels in the neuroexcitatory and synergistic effects of putative uremic neurotoxins. Kidney Int. 2003;63:1764–75.
De Deyn PP, Macdonald RL. Guanidino compounds that are increased in cerebrospinal fluid and brain of uremic patients inhibit GABA and glycine responses on mouse neurons in cell culture. Ann Neurol. 1990;28:627–33.
Rueth M, Lemke H-D, Preisinger C, Krieter D, Theelen W, Gajjala P, et al. Guanidinylations of albumin decreased binding capacity of hydrophobic metabolites. Acta Physiol. 2015;215:13–23.
Acknowledgements
Jente Boelaert gratefully acknowledges the financial support of the Agency for Innovation by Science and Technology in Flanders (IWT). The Metabolomics Innovation Centre (TMIC, Canada) is acknowledged for providing metabolite standards.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Informed consent
The study protocol was approved by the Ethics Committee of Gent University Hospital, and informed consent was obtained prior to collection of the samples.
Rights and permissions
About this article
Cite this article
Boelaert, J., Lynen, F., Glorieux, G. et al. Metabolic profiling of human plasma and urine in chronic kidney disease by hydrophilic interaction liquid chromatography coupled with time-of-flight mass spectrometry: a pilot study. Anal Bioanal Chem 409, 2201–2211 (2017). https://doi.org/10.1007/s00216-016-0165-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00216-016-0165-x