Development of a Two-Dimensional Liquid Chromatographic Method for Analysis of Urea Cycle Amino Acids

Abstract

The urea cycle has been found to be closely associated with certain types of cancers and other diseases such as cardiovascular disease and chronic kidney disease. An analytical method for the precise quantification of urea cycle amino acids (arginine, ornithine, citrulline, and argininosuccinate) by off-line two-dimensional liquid chromatography (2D-LC) combined with fluorescence-based detection was developed. Before analysis, the amino acids were derivatised with 4-fluoro-7-nitro-2,1,3-benzoxadiazole (NBD-F) to obtain NBD-amino acids. The first dimension involved the reversed-phase separation, in which NBD derivatives of urea cycle amino acids were completely separated from each other and mostly separated from the 18 NBD-proteinogenic amino acids. The samples were eluted with stepwise gradient using 0.02% trifluoroacetic acid in water–acetonitrile as the mobile phase. In the second dimension, an amino column was used for the separation of NBD-ornithine, -citrulline, and -argininosuccinate, while a sulfonic acid column was used to separate NBD-arginine. The developed 2D-LC system was used to analyse human plasma samples. The fractions of NBD-urea cycle amino acids obtained in the first dimension were collected manually and introduced into the second dimension. By choosing appropriate mobile phases for the second dimension, each NBD-urea cycle amino acid eluted in the first dimension was well separated from the other proteinogenic amino acids and interference from endogenous substance. This could not be achieved in the first dimension. The urea cycle amino acids in human plasma sample were quantified, and the method was well validated. The calibration curves for each NBD-urea cycle amino acid showed good linearity from 3 (ASA) or 15 (Orn, Cit, and Arg) to 600 nM, with correlation coefficients higher than 0.9969. The intraday and interday precisions were less than 7.9% and 15%, respectively. The 2D-LC system is expected to be useful for understanding the involvement of the urea cycle in disease progression.