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Background: Urinalysis is an important component in the assessment of acute kidney injury (AKI). Proteonomics is a rapidly developing approach in the analysis of physiological states. Several techniques have been developed to screen for protein populations. In this regard SELDI-TOF is a technique based on mass spectroscopy that is being utilized in proteonomics research.

Methods:For this study, clean catch or catheterized urine was collected from normals (n=18) and patients referred to the renal service with AKI. Based upon urine and serum chemistries, clinical parameters, and microscopic urinalysis, the urines were separated into those consistent with prerenal azotemia (n=17) and acute tubular necrosis (ATN) (n=29). Initially, 5 samples each were chosen from the pre-renal and ATN who had no preexisting renal disease. Other etiologies of AKI were not included in this analysis. The urine specimens were diluted 1:5 and deposited onto an H4 ProteinChip array using 50% acetonitrile as the binding buffer. This system captured the greatest spectral range with the SELDI-TOF evaluation (compared to SAX, WCX2, IMAC, and NP1 ProteinChips). Low (250) and high (300) laser intensities were utilized to ionize and desorb the protein molecules; the spectra were collected in a positive ion mode and analyzed with Ciphergen Peaks software (v 3.0).

Results: Five peaks with the high laser power were identified as potential candidates to discriminate between AKI due to prerenal or ATN causes. Those urines from the prerenal subjects were associated with detectable masses at 22.6 and 44.8 kilodaltons (KD); whereas subjects with ATN were noted to have urine with substantial masses at 11, 11.7, and 14.6 KD. The intensity of these peaks were then added together and normalized with the individual components of the discriminate peaks representing a percentage of the total. The prerenal and ATN subjects were then randomized in a training set consisting of 23 subjects and a testing set consisting of 23 subjects. Multiple linear regression was performed on the training set, and this allowed for 65% accuracy when applied to the testing set. Feed forward neural networks with hidden neuron layers ranging from 2-10 achieved similar predictive capability on the training set and testing sets.

Conclusions: Although the SELDI-TOF methodology may be a useful adjunct in the assessment of AKI and renal disease, we suggest that larger training sets will be necessary to effectively exploit this strategy.

Conflict(s) of Interest


References with DOI

1. Miller TR, Anderson RJ, Linas SL, Henrich WL, Berns AS, Gabow PA and Schrier RW. Urinary diagnostic indices in acute kidney injury:a prospective study. Ann Intern Med. 1978;89:47-50. https://doi.org/10.7326/0003-4819-89-1-47

2. Trof RJ, Di Maggio F, Leemreis J and Groeneveld AB. Biomarkers of acute renal injury and renal failure. Shock. 2006;26:245-53. https://doi.org/10.1097/01.shk.0000225415.5969694.ce

3. Adiyanti SS and Loho T. Acute kidney injury (AKI) biomarker. Acta Med Indones. 2012;44:246-55.

4. Hampel DJ, Sansome C, Sha M, Brodsky S, Lawson WE and Goligorsky MS. Toward proteomics inuroscopy: urinary protein profiles after radiocontrast medium administration. J Am Soc Nephrol. 2001;12:1026-35.

5. Jacob AN, Khuder S, Malhotra N, Sodeman T, Gold JP, Malhotra D and Shapiro JI. Neural network analysis to predict mortality in end-stage renal disease: application to United States renal data system. Nephron Clin Pract. 2010;116:c148-58. https://doi.org/10.1159/000315884

6. Luk CC, Chow KM, Kwok JS, Kwan BC, Chan MH, Lai KB, Lai FM, Wang G, Li PK and Szeto CC. Urinary biomarkers for the prediction of reversibility in acute-on-chronic renal failure. Dis Markers. 2013;34:179-85. https://doi.org/10.1155/2013/349545

7. Gomes E, Antunes R, Dias C, Araujo R and Costa-Pereira A. Acute kidney injury in severe trauma assessed by RIFLE criteria: a common feature without implications on mortality? Scand J Trauma Resusc Emerg Med. 2010;18:1. https://doi.org/10.1186/1757-7241-18-1

8. Hepburn S, Cairns DA, Jackson D, Craven RA, Riley B, Hutchinson M, Wood S, Smith MW, Thompson D and Banks RE. An analysis of the impact of pre-analytical factors on the urine proteome: Sample processing time, temperature, and proteolysis. Proteomics Clin Appl. 2015;9:507-21. https://doi.org/10.1002/prca.201400079

9. Alves G, Pereira DA, Sandim V, Ornellas AA, Escher N, Melle C and von Eggeling F. Urine screening by Seldi-Tof, followed by biomarker identification, in a Brazilian cohort of patients with renal cell carcinoma (RCC). Int Braz J Urol. 2013;39:228-39.

10. Bellei E, Monari E, Cuoghi A, Bergamini S, Guerzoni S, Ciccarese M, Ozben T, Tomasi A and Pini LA. Discovery by a proteomic approach of possible early biomarkers of drug-induced nephrotoxicity in medication-overuse headache. J Headache Pain. 2013;14:6. https://doi.org/10.1186/1129-2377-14-6

11. Woodbury RL, McCarthy DL and Bulman AL. Profiling of urine using ProteinChip(R) technology. Methods Mol Biol. 2012;818:97-107. https://doi.org/10.1007/978-1-61779-418-6_7

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