A pilot study of GC/MS-based serum metabolic profiling of acute rejection in renal transplantation

• Published in: Transplant immunology 2008-04, Vol.19 (1), p.74-80
• Main Authors: Mao, You-ying, Bai, Jing-qing, Chen, Jiang-hua, Shou, Zhang-fei, He, Qiang, Wu, Jian-yong, Chen, Ying, Cheng, Yi-yu
• Format: Article
• Language: English
• Subjects: Acute allograft rejection; Adult; Allergy and Immunology; Amino Acids - blood; Carbohydrates - blood; Carboxylic Acids - blood; Female; Gas chromatograph-mass spectrometry (GC-MS); Gas Chromatography-Mass Spectrometry; Graft Rejection - blood; Graft Rejection - immunology; Humans; Kidney transplantation; Kidney Transplantation - immunology; Male; Metabolic profiling; Metabolism; Metabolomics; Middle Aged; Pilot Projects; Principal Component Analysis
• ISSN: 0966-3274; 1878-5492
• DOI: 10.1016/j.trim.2008.01.006

Abstract Aims Acute allograft rejection is one of the important complications after renal transplantation, and it is a deleterious factor for long-term graft survival. Rejection is a complex pathophysiologic process, which has been explained by transcriptome and proteome in RNA transcripts and proteins level respectively. How are serum metabolite levels in response to acute rejection? Can metabolite levels in serum be used to diagnose and explain acute renal allograft rejection? Methods Gas chromatograph-mass spectrometry (GC-MS) was used to analyze serum metabolome in 22 recipients of acute rejection and 15 stable renal transplant recipients. Results 46 endogenous metabolites included amino acid, fatty acid, carbohydrate and other intermediate metabolites were identified in 37 recipients. Principal component analysis based on these metabolites discriminated acute rejection group from stable recipients. Among these metabolites, the levels of 17 metabolites were significant higher in rejection group than those in stable group. These included amino acid (phenylalanine, serine, glycine, threonine, valine), carbohydrate (galactose oxime, glycose, fructose), carboxylic acid, lipids and other metabolite such as lactate, urea and myo-inositol. The levels of 5 metabolites of alanine, lysine, leucine, aminomalonic acid and tetradecanoic acid were low in rejection group compared to stable group. The prediction accuracy of acute rejection was 77.3% and stable function was 100% by supervised clustering based on these 22 metabolites. Conclusions This study demonstrated that metabolic profile was changed in response to rejection process and renal function can be reflected by serum metabolite levels. This study showed potential capability to diagnose acute rejection by metabolome analysis.