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Metabolomics analysis of serum in pediatric nephrotic syndrome based on targeted and non-targeted platforms

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Abstract

Background and aims

Nephrotic syndrome (NS) is a common pediatric urinary system disease. The aim in this work was to investigate the changes in pediatric NS-related metabolites through serum metabolomics, and explore the new potential metabolites and differential metabolic pathways.

Methods

Serum samples from 40 pediatric patients with nephrotic syndrome and 40 healthy controls were collected. The targeted and non-targeted metabolomics analyses were performed to determine the metabolic changes in pediatric NS. Based on multivariate statistical analysis and the regression model, the serum potential metabolites were screened and different metabolic pathways were explored.

Results

39 differential metabolites in pediatric NS were obtained based on the metabolomics analysis. 12 differential metabolites (serine, C18: 2 (EFA), C18: 2 (FFA), Isonuatigenin 3- [rhamnosyl- (1- > 2) -glucoside], C18: 4 (EFA), C18: 4 (FFA), caprylic acid, citric acid, methylmalonic acid, caproic acid, canavalioside and uroporphyrin were identified to establish the diagnostic model for pediatric NS. Five metabolic pathways including TCA cycle, amino acid metabolism, bile acid biosynthesis, linoleate metabolism and glyoxylate and dicarboxylate metabolism were the key differential metabolic pathways.

Conclusion

These data elucidated the metabolic alterations associated with pediatric NS and suggested a new diagnosis model for monitoring pediatric NS. The current study provides the useful information to bridge the gaps in our understanding of the metabolic alterations associated with pediatric NS and might facilitate the characterization of pediatric NS patients by performing serum metabolomics.

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Data Availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Abbreviations

ALB:

Albumin

AUC:

The area under the ROC curve

EFA:

Esterified fatty acids

eGFR:

Estimated glomerular filtration rate

EP:

Eppendorf

ESI-TQ-MS:

Electrospray ionization-triple quadrupole-mass spectrometry

ESRD:

End stage renal disease

FDR:

False discovery rate

FFA:

Free fatty acids

GC–MS:

Gas chromatography-mass spectrometry

GLU:

Glucose

HDL-C:

High density lipoprotein-cholesterol

LC–MS:

Liquid chromatography-mass spectrometry

LDL-C:

Low density lipoprotein-cholesterol

PCA:

Principal component analysis

PLS-DA:

Partial least-squares-discriminant analysis

QC:

Quality control

ROC:

Receiver operating characteristic

TCA:

Tricarboxylic acid

TCHO:

Total cholesterol

TG:

Triglyceride

Up/Ucr:

Urine protein-creatinine ratio

UPLC-MS:

Ultra-performance liquid chromatography-mass spectrometry

UPLC-Q-TOF/MS:

Ultra-performance liquid chromatography-quadrupole-time of flight-mass spectrometry

UPLC-TQ-MS:

Ultra-performance liquid chromatography-triple quadrupole-mass spectrometry

VIP:

Variable importance in the projection

References

  • Abbiss, H., Maker, G. L., Gummer, J. P. A., Rawlinson, C., Musk, G. C., Fleming, P. A., Phillips, J. K., Boyce, M. C., & Trengove, R. D. (2019a). Untargeted gas chromatography–mass spectrometry-based metabolomics analysis of kidney and liver tissue from the Lewis Polycystic Kidney rat. Journal of Chromatography B, 1118–1119, 25–32.

    Article  Google Scholar 

  • Abbiss, H., Maker, G. L., & Trengove, R. D. (2019b). Metabolomics approaches for the diagnosis and understanding of kidney diseases. Metabolites, 9, 2.

    Article  Google Scholar 

  • Adams, D. R., Tollinche, L. E., Yeoh, C. B., Artman, J., Mehta, M., Phillips, D., Fischer, G. W., Quinlan, J. J., & Sakai, T. (2020). Short-term safety and effectiveness of sugammadex for surgical patients with end-stage renal disease: A two-centre retrospective study. Anaesthesia, 75, 348–352.

    Article  CAS  Google Scholar 

  • Anzmann, A. F., Pinto, S., Busa, V., Carlson, J., McRitchie, S., Sumner, S., Pandey, A., & Vernon, H. J. (2019). Multi-omics studies in cellular models of methylmalonic acidemia and propionic acidemia reveal dysregulation of serine metabolism. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1865, 165538.

    Article  CAS  Google Scholar 

  • Bujak, R., Struck-Lewicka, W., Markuszewski, M. J., & Kaliszan, R. (2015). Metabolomics for laboratory diagnostics. Journal of Pharmaceutical and Biomedical Analysis, 113, 108–120.

    Article  CAS  Google Scholar 

  • Byer, K., & Khan, S. R. (2005). Citrate provides protection against oxalate and calcium oxalate crystal induced oxidative damage to renal epithelium. The Journal of Urology, 173, 640–646.

    Article  CAS  Google Scholar 

  • Carter, S. A., Mistry, S., Fitzpatrick, J., Banh, T., Hebert, D., Langlois, V., Pearl, R. J., Chanchlani, R., Licht, C. P. B., Radhakrishnan, S., Brooke, J., Reddon, M., Levin, L., Aitken-Menezes, K., Noone, D., & Parekh, R. S. (2020). Prediction of short- and long-term outcomes in childhood nephrotic syndrome. Kidney Int Rep, 5, 426–434.

    Article  Google Scholar 

  • Chou, J., Liu, R., Yu, J., Liu, X., Zhao, X., Li, Y., Liu, L., & Sun, C. (2018). Fasting serum α-hydroxybutyrate and pyroglutamic acid as important metabolites for detecting isolated post-challenge diabetes based on organic acid profiles. Journal of Chromatography B, 1100–1101, 6–16.

    Article  Google Scholar 

  • Davis, R. A., Miyake, J. H., Hui, T. Y., & Spann, N. J. (2002). Regulation of cholesterol-7alpha-hydroxylase: BAREly missing a SHP. Journal of Lipid Research, 43, 533–543.

    Article  CAS  Google Scholar 

  • Di Ciaula, A., Garruti, G., Lunardi Baccetto, R., Molina-Molina, E., Bonfrate, L., Wang, D. Q. H., & Portincasa, P. (2017). Bile acid physiology. Annals of Hepatology, 16, S4–S14.

    Article  Google Scholar 

  • Fanos, V., Fanni, C., Ottonello, G., Noto, A., Dessi, A., & Mussap, M. (2013). Metabolomics in adult and pediatric nephrology. Molecules, 18, 4844–4857.

    Article  CAS  Google Scholar 

  • Feltran, L. S., Watanabe, A., Guaragna, M. S., Machado, I. C., Casimiro, F. M. S., Neves, P. D. M. M., Palma, L. M., Varela, P., Vaisbich, M. H., Marie, S. K. N., Facincani, I., Pesquero, J. B., Belangero, V. M. S., Sampson, M. G., Koch Nogueira, P. C., & Onuchic, L. F. (2019). Brazilian network of pediatric nephrotic syndrome (REBRASNI). Kidney International Reports, 5, 3.

    Google Scholar 

  • Gagnebin, Y., Julien, B., Belen, P., & Serge, R. (2018). Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage. Journal of Pharmaceutical and Biomedical Analysis, 161, 313–325.

    Article  Google Scholar 

  • Gao, H., Yu, X., Sun, R., Yang, N., He, J., Tao, M., Gu, H., Yan, C., Aa, J., & Wang, G. (2018). Quantitative GC–MS assay of citric acid from humans and db/db mice blood serum to assist the diagnosis of diabetic nephropathy. Journal of Chromatography B, 1077–1078, 28–34.

    Article  Google Scholar 

  • Gooding, J. R., Agrawal, S., McRitchie, S., Acuff, Z., Merchant, M. L., Klein, J. B., Smoyer, W. E., Sumner, S. J., Mahan, J., Patel, H., Ransom, R. F., Pan, C., Geary, D. F., Chang, M. L., Gibson, K. L., Iorember, F. M., Brophy, P. D., Srivastava, T., & Greenbaum, L. A. (2020). Predicting and defining steroid resistance in pediatric nephrotic syndrome using plasma metabolomics. Kidney International Reports, 5, 81–93.

    Article  Google Scholar 

  • Hallan, S., Afkarian, M., Zelnick, L. R., Kestenbaum, B., Sharma, S., Saito, R., Darshi, M., Barding, G., Raftery, D., Ju, W., Kretzler, M., Sharma, K., & de Boer, I. H. (2017). Metabolomics and gene expression analysis reveal down-regulation of the citric acid (TCA) cycle in non-diabetic CKD patients. eBioMedicine, 26, 68–77.

    Article  Google Scholar 

  • Hasegawa, H., Masuda, N., Natori, H., Shinohara, Y., & Ichida, K. (2019). Pharmacokinetics and toxicokinetics of d-serine in rats. Journal of Pharmaceutical and Biomedical Analysis, 162, 264–271.

    Article  CAS  Google Scholar 

  • Kalim, S., & Rhee, E. P. (2017). An overview of renal metabolomics. Kidney International, 91, 61–69.

    Article  CAS  Google Scholar 

  • Kunze, M., Pracharoenwattana, I., Smith, S. M., & Hartig, A. (2006). A central role for the peroxisomal membrane in glyoxylate cycle function. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research, 1763, 1441–1452.

    Article  CAS  Google Scholar 

  • Lazarević, S., Đanić, M., Goločorbin-Kon, S., Al-Salami, H., & Mikov, M. (2019). Semisynthetic bile acids: A new therapeutic option for metabolic syndrome. Pharmacological Research, 146, 104333.

    Article  Google Scholar 

  • Lee, J.-E., Lee, Y. H., Kim, S.-Y., Kim, Y. G., Moon, J.-Y., Jeong, K.-H., Lee, T. W., Ihm, C.-G., Kim, S., Kim, K. H., Kim, D. K., Kim, Y. S., Kim, C.-D., Park, C. W., Lee, D. Y., & Lee, S.-H. (2016). Systematic biomarker discovery and coordinative validation for different primary nephrotic syndromes using gas chromatography–mass spectrometry. Journal of Chromatography A, 1453, 105–115.

    Article  CAS  Google Scholar 

  • Li, A., Zhang, W., Zhang, L., Liu, Y., Li, K., Du, G., & Qin, X. (2020). Elucidating the time-dependent changes in the urinary metabolome under doxorubicin-induced nephrotoxicity. Toxicology Letters, 319, 204–212.

    Article  CAS  Google Scholar 

  • Liu, L., Feng, R., Guo, F., Li, Y., Jiao, J., & Sun, C. (2015). Targeted metabolomic analysis reveals the association between the postprandial change in palmitic acid, branched-chain amino acids and insulin resistance in young obese subjects. Diabetes Research and Clinical Practice, 108, 84–93.

    Article  CAS  Google Scholar 

  • Liu, L., Li, Y., Guan, C., Li, K., Wang, C., Feng, R., & Sun, C. (2010a). Free fatty acid metabolic profile and biomarkers of isolated post-challenge diabetes and type 2 diabetes mellitus based on GC-MS and multivariate statistical analysis. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 878, 2817–2825.

    Article  CAS  Google Scholar 

  • Liu, L., Li, Y., Guan, C., Li, K., Wang, C., Feng, R., & Sun, C. (2010b). Free fatty acid metabolic profile and biomarkers of isolated post-challenge diabetes and type 2 diabetes mellitus based on GC–MS and multivariate statistical analysis. Journal of Chromatography B, 878, 2817–2825.

    Article  CAS  Google Scholar 

  • Liu, R., Chou, J., Hou, S., Liu, X., Yu, J., Zhao, X., Li, Y., Liu, L., & Sun, C. (2018). Evaluation of two-step liquid-liquid extraction protocol for untargeted metabolic profiling of serum samples to achieve broader metabolome coverage by UPLC-Q-TOF-MS. Analytica Chimica Acta, 1035, 96–107.

    Article  CAS  Google Scholar 

  • Mak, S. K., Short, C. D., & Mallick, N. P. (1996). Long-term outcome of adult-onset minimal-change nephropathy. Nephrology, Dialysis, Transplantation, 11, 2192–2201.

    Article  CAS  Google Scholar 

  • Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). “Metabonomics”: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29, 1181–1189.

    Article  CAS  Google Scholar 

  • Orozco-Ibarra, M., Medina-Campos, O. N., Sánchez-González, D. J., Martínez-Martínez, C. M., Floriano-Sánchez, E., Santamaría, A., Ramirez, V., Bobadilla, N. A., & Pedraza-Chaverri, J. (2007). Evaluation of oxidative stress in d-serine induced nephrotoxicity. Toxicology, 229, 123–135.

    Article  CAS  Google Scholar 

  • Sedic, M., Gethings, L. A., Vissers, J. P. C., Shockcor, J. P., McDonald, S., Vasieva, O., Lemac, M., Langridge, J. I., Batinić, D., & Pavelić, S. K. (2014). Label-free mass spectrometric profiling of urinary proteins and metabolites from paediatric idiopathic nephrotic syndrome. Biochemical and Biophysical Research Communications, 452, 21–26.

    Article  CAS  Google Scholar 

  • Trautmann, A., Vivarelli, M., Samuel, S., Gipson, D., Sinha, A., Schaefer, F., Hui, N. K., Boyer, O., Saleem, M. A., Feltran, L., Muller-Deile, J., Becker, J. U., Cano, F., Xu, H., Lim, Y. N., Smoyer, W., Anochie, I., Nakanishi, K., Hodson, E., … International Pediatric Nephrology, A. (2020). IPNA clinical practice recommendations for the diagnosis and management of children with steroid-resistant nephrotic syndrome. Pediatr Nephrol, 35, 1529–1561.

    Article  Google Scholar 

  • Vaziri, N. D. (2016). Disorders of lipid metabolism in nephrotic syndrome: mechanisms and consequences. Kidney International, 90, 41–52.

    Article  CAS  Google Scholar 

  • Vuoristo, K. S., Mars, A. E., Sanders, J. P. M., Eggink, G., & Weusthuis, R. A. (2016). Metabolic engineering of TCA cycle for production of chemicals. Trends in Biotechnology, 34, 191–197.

    Article  CAS  Google Scholar 

  • Wang, C.-S., & Greenbaum, L. A. (2019). Nephrotic syndrome. Pediatric Clinics of North America, 66, 73–85.

    Article  CAS  Google Scholar 

  • Williams, R. E., Major, H., Lock, E. A., Lenz, E. M., & Wilson, I. D. (2005). d-Serine-induced nephrotoxicity: a HPLC–TOF/MS-based metabonomics approach. Toxicology, 207, 179–190.

    Article  CAS  Google Scholar 

  • Wishart, D. S. (2019). Metabolomics for investigating physiological and pathophysiological processes. Physiological Reviews, 99, 1819–1875.

    Article  CAS  Google Scholar 

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Acknowledgement

The youth science foundation of Liande Wu in Harbin Medical University (NO.WLD-QN1704). Postdoctoral Science Foundation of Heilongjiang Province (No.LBH-Q17090).

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Authors and Affiliations

  1. Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, 150086, People’s Republic of China

    Jing Guo, Jinhui Zhao, Jiaying Yu, Mingjia Zhang & Liyan Liu

  2. The Department of Clinical Nutrition, Southern University of Science and Technology Hospital, Shenzhen, People’s Republic of China

    Rui Liu

  3. Department of Infectious Diseases, Harbin Children’s Hospital, 57 Youyi Road, Daoli District, Harbin, People’s Republic of China

    Hanming Wang

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  1. Jing Guo
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  2. Jinhui Zhao
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  7. Liyan Liu
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Contributions

JG: Conceptualization, Methodology, Formal analysis, Investigation, Writing—original draft preparation, Writing—review and editing; JZ: Formal analysis, Writing—original draft preparation; RL: Resources, Investigation; MZ: Conceptualization, Methodology; HW: Resources, Supervision; JY: Conceptualization, Supervision; LL: Funding acquisition, Writing—original draft preparation, Writing—review and editing, Supervision.

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Correspondence to Liyan Liu.

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Guo, J., Zhao, J., Liu, R. et al. Metabolomics analysis of serum in pediatric nephrotic syndrome based on targeted and non-targeted platforms. Metabolomics 17, 38 (2021). https://doi.org/10.1007/s11306-021-01788-1

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