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.
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.
Authors: Luis F Hernandez; Luis R Betancourt; Ernesto S Nakayasu; Charles Ansong; Gerardo A Ceballos; Daniel Paredes; Midhat H Abdulreda Journal: Int J Mol Sci Date: 2021-08-15 Impact factor: 6.208
Authors: Paulin N Wahjudi; Mary E Patterson; Shu Lim; Jennifer K Yee; Catherine S Mao; W-N Paul Lee Journal: Clin Biochem Date: 2009-09-08 Impact factor: 3.281
Authors: Warwick B Dunn; Wanchang Lin; David Broadhurst; Paul Begley; Marie Brown; Eva Zelena; Andrew A Vaughan; Antony Halsall; Nadine Harding; Joshua D Knowles; Sue Francis-McIntyre; Andy Tseng; David I Ellis; Steve O'Hagan; Gill Aarons; Boben Benjamin; Stephen Chew-Graham; Carly Moseley; Paula Potter; Catherine L Winder; Catherine Potts; Paula Thornton; Catriona McWhirter; Mohammed Zubair; Martin Pan; Alistair Burns; J Kennedy Cruickshank; Gordon C Jayson; Nitin Purandare; Frederick C W Wu; Joe D Finn; John N Haselden; Andrew W Nicholls; Ian D Wilson; Royston Goodacre; Douglas B Kell Journal: Metabolomics Date: 2014-07-25 Impact factor: 4.290
Authors: Pauline Erpicum; Oriane Hanssen; Laurent Weekers; Pierre Lovinfosse; Paul Meunier; Luaba Tshibanda; Jean-Marie Krzesinski; Roland Hustinx; François Jouret Journal: Clin Kidney J Date: 2016-09-06