Preterm neonatal urinary renal developmental and acute kidney injury metabolomic profiling: an exploratory study
- 693 Downloads
Acute kidney injury (AKI) staging has been developed in the adult and pediatric populations, but these do not yet exist for the neonatal population. Metabolomics was utilized to uncover biomarkers of normal and AKI-associated renal function in preterm infants. The study comprised 20 preterm infants with an AKI diagnosis who were matched by gestational age and gender to 20 infants without an AKI diagnosis.
Urine samples from pre-term newborn infants collected on day 2 of life were analyzed using broad-spectrum nuclear magnetic resonance (NMR) metabolomics. Multivariate analysis methods were used to identify metabolite profiles that differentiated AKI and no AKI, and to identify a metabolomics profile correlating with gestational age in infants with and without AKI.
There was a clear distinction between the AKI and no-AKI profiles. Two previously identified biomarkers of AKI, hippurate and homovanillate, differentiated AKI from no-AKI profiles. Pathway analysis revealed similarities to cholinergic neurons, prenatal nicotine exposure on pancreatic β cells, and amitraz-induced inhibition of insulin secretion. Additionally, a pH difference was noted. Both pH and the metabolites were found to be associated with AKI; however, only the metabotype was a significant predictor of AKI. Pathways for the no-AKI group that correlated uniquely with gestational age included aminoacyl-t-RNA biosynthesis, whereas pathways in the AKI group yielded potential metabolite changes in pyruvate metabolism.
Metabolomics was able to differentiate the urinary profiles of neonates with and without an AKI diagnosis and metabolic developmental profiles correlated with gestational age. Further studies in larger cohorts are needed to validate these results.
KeywordsNeonatal Acute kidney injury Renal development Metabolomics NMR spectroscopy Multivariate analysis Regression analysis
We thank Zachery Acuff and Dr Jason Burgess for their contributions to this study. This project was performed as a collaboration through the NIH Eastern Regional Comprehensive Metabolomics Resource Core (RTI RCMRC), a NIH Common Fund award through NIDDK, Project Number 1U24DK097193-01 (Sumner), the Norman Siegel Career Development Award from the American Society of Nephrology/The UAB Center for Clinical and Translational Science UL1 TR000165 (Askenazi), and the University of Iowa Institute for Clinical and Translational Science UL1RR024979 (Brophy).
Compliance with ethical standards
This study was approved by the University of Alabama at Birmingham (UAB) Institutional Review Board; informed parental consent was obtained for all infants. The clinical and research activities being reported are consistent with the principles of the Declaration of Helsinki.
Conflicts of interest
The authors declare that they have no conflicts of interest.
- 11.Morrow AL, Lagomarcino AJ, Schibler KR, Taft DH, Yu Z, Wang B, Altaye M, Wagner M, Gevers D, Ward DV, Kennedy MA, Huttenhower C, Newburg DS (2013) Early microbial and metabolomic signatures predict later onset of necrotizing enterocolitis in preterm infants. Microbiome 1:13CrossRefPubMedPubMedCentralGoogle Scholar
- 12.Barberini L, Noto A, Fattuoni C, Grapov D, Casanova A, Fenu G, Gaviano M, Carboni R, Ottonello G, Crisafulli M, Fanos V, Dessi A (2014) Urinary metabolomics (GC-MS) reveals that low and high birth weight infants share elevated inositol concentrations at birth. J Matern Fetal Neonatal Med 27 [Suppl 2]:20–26CrossRefPubMedGoogle Scholar
- 15.Roux A, Thévenot EA, Seguin F, Olivier M-F, Junot C (2015) Impact of collection conditions on the metabolite content of human urine samples as analyzed by liquid chromatography coupled to mass spectrometry and nuclear magnetic resonance spectroscopy. Metabolomics 11:1095–1105CrossRefPubMedGoogle Scholar
- 16.Sumner SC, Snyder RW, Wingard C, Mortensen NP, Holland NA, Shannahan JH, Dhungana S, Pathmasiri W, Han L, Lewin AH, Fennell TR (2015) Distribution and biomarkers of carbon-14-labeled fullerene C60 ([(14) C(U)]C60 ) in female rats and mice for up to 30 days after intravenous exposure. J Appl Toxicol 35:1452–1464CrossRefPubMedGoogle Scholar
- 18.Sumner S, Snyder R, Burgess J, Myers C, Tyl R, Sloan C, Fennell T (2009) Metabolomics in the assessment of chemical-induced reproductive and developmental outcomes using non-invasive biological fluids: application to the study of butylbenzyl phthalate. J Appl Toxicol 29:703–714CrossRefPubMedPubMedCentralGoogle Scholar
- 19.Church RJ, Wu H, Mosedale M, Sumner SJ, Pathmasiri W, Kurtz CL, Pletcher MT, Eaddy JS, Pandher K, Singer M, Batheja A, Watkins PB, Adkins K, Harrill AH (2014) A systems biology approach utilizing a mouse diversity panel identifies genetic differences influencing isoniazid- induced microvesicular steatosis. Toxicol Sci 140:481–492CrossRefPubMedPubMedCentralGoogle Scholar
- 21.Snyder RW, Fennell TR, Wingard CJ, Mortensen NP, Holland NA, Shannahan JH, Pathmasiri W, Lewin AH, Sumner SC (2015) Distribution and biomarker of carbon-14 labeled fullerene C ([ C(U)]C ) in pregnant and lactating rats and their offspring after maternal intravenous exposure. J Appl Toxicol 35:1438–1451CrossRefPubMedGoogle Scholar
- 22.Bylesjö M, Rantalainen M, Cloarec O, Nicholson JK, Holmes E, Trygg J (2006) OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Proteome Res 20:341–351Google Scholar
- 23.Eriksson L, Byrne T, Johansson E, Trygg J, Vikström C (2013) Multi-and megavariate data analysis basic principles and applications. Umetrics Academy, SwedenGoogle Scholar
- 25.Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, Cheng D, Jewell K, Arndt D, Sawhney S, Fung C, Nikolai L, Lewis M, Coutouly MA, Forsythe I, Tang P, Shrivastava S, Jeroncic K, Stothard P, Amegbey G, Block D, Hau DD, Wagner J, Miniaci J, Clements M, Gebremedhin M, Guo N, Zhang Y, Duggan GE, Macinnis GD, Weljie AM, Dowlatabadi R, Bamforth F, Clive D, Greiner R, Li L, Marrie T, Sykes BD, Vogel HJ, Querengesser L (2007) HMDB: the Human Metabolome Database. Nucleic Acids Res 35:D521–D526CrossRefPubMedPubMedCentralGoogle Scholar
- 26.Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD, Psychogios N, Dong E, Bouatra S, Mandal R, Sinelnikov I, Xia J, Jia L, Cruz JA, Lim E, Sobsey CA, Shrivastava S, Huang P, Liu P, Fang L, Peng J, Fradette R, Cheng D, Tzur D, Clements M, Lewis A, De Souza A, Zuniga A, Dawe M, Xiong Y, Clive D, Greiner R, Nazyrova A, Shaykhutdinov R, Li L, Vogel HJ, Forsythe I (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37:D603–D610CrossRefPubMedGoogle Scholar
- 27.Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A (2013) HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res 41:D801–D807CrossRefPubMedGoogle Scholar
- 32.Kim KB, Um SY, Chung MW, Jung SC, Oh JS, Kim SH, Na HS, Lee BM, Choi KH (2010) Toxicometabolomics approach to urinary biomarkers for mercuric chloride (HgCl(2))-induced nephrotoxicity using proton nuclear magnetic resonance ((1)H NMR) in rats. Toxicol Appl Pharmacol 249:114–126CrossRefPubMedGoogle Scholar
- 51.Moltu SJ, Sachse D, Blakstad EW, Strommen K, Nakstad B, Almaas AN, Westerberg AC, Ronnestad A, Braekke K, Veierod MB, Iversen PO, Rise F, Berg JP, Drevon CA (2014) Urinary metabolite profiles in premature infants show early postnatal metabolic adaptation and maturation. Nutrients 6:1913–1930CrossRefPubMedPubMedCentralGoogle Scholar
- 52.Dessi A, Atzori L, Noto A, Visser GH, Gazzolo D, Zanardo V, Barberini L, Puddu M, Ottonello G, Atzei A, De Magistris A, Lussu M, Murgia F, Fanos V (2011) Metabolomics in newborns with intrauterine growth retardation (IUGR): urine reveals markers of metabolic syndrome. J Matern Fetal Neonatal Med 24 [Suppl 2]:35–39CrossRefPubMedGoogle Scholar
- 55.Feldkamp T, Park JS, Pasupulati R, Amora D, Roeser NF, Venkatachalam MA, Weinberg JM (2009) Regulation of the mitochondrial permeability transition in kidney proximal tubules and its alteration during hypoxia-reoxygenation. Am J Physiol Renal Physiol 297:F1632–F1646CrossRefPubMedPubMedCentralGoogle Scholar