BACKGROUND: Peritoneal dialysis (PD) is one of the therapeutic options for the end-stage renal disease (ESRD) patients. The peritoneal membrane is immersed in a high glucose concentration of the peritoneal dialysate, which may cause structural and functional damage. Peritonitis is one of the major complications of PD, which will accelerate the damage of peritoneal membrane by increasing the peritoneal permeability and decrease the ultrafiltration efficiency. It will cause the peritoneal membrane dysfunction and the patient may have fluid overload related complications or hemodialysis. METHODS: To enhance our understanding of peritoneal dialysate, the peritoneal dialysate proteins were identified by 2-dimensional gel electrophoresis (2DE) combined with reverse phase nano-high performance liquid chromatography electrospray ionization tandem mass spectrometry (RP-nano-HPLC-ESI-MS/MS) followed by peptide fragmentation pattern. RESULTS: We performed 2DE on the 12 peritoneal dialysate before/after peritonitis, and more than 350 spots were detected. Among these protein spots, 136 spots of the 2DE were excised, in-gel digested and identified by nano-HPLC-ESI-MS/MS. A total of 41 proteins were identified with high levels of confidence. Ten of these were significantly differentially expressed between the peritoneal dialysate samples before/after peritonitis. CONCLUSION: The present study was designed to establish optimal techniques to develop a proteomic map of peritoneal dialysate proteins. These proteins may not be new biomarkers; however, they may indicate a situation for possible drug treatment and can be the predictors of peritonitis for the validation study in the further.
BACKGROUND: Peritoneal dialysis (PD) is one of the therapeutic options for the end-stage renal disease (ESRD) patients. The peritoneal membrane is immersed in a high glucose concentration of the peritoneal dialysate, which may cause structural and functional damage. Peritonitis is one of the major complications of PD, which will accelerate the damage of peritoneal membrane by increasing the peritoneal permeability and decrease the ultrafiltration efficiency. It will cause the peritoneal membrane dysfunction and the patient may have fluid overload related complications or hemodialysis. METHODS: To enhance our understanding of peritoneal dialysate, the peritoneal dialysate proteins were identified by 2-dimensional gel electrophoresis (2DE) combined with reverse phase nano-high performance liquid chromatography electrospray ionization tandem mass spectrometry (RP-nano-HPLC-ESI-MS/MS) followed by peptide fragmentation pattern. RESULTS: We performed 2DE on the 12 peritoneal dialysate before/after peritonitis, and more than 350 spots were detected. Among these protein spots, 136 spots of the 2DE were excised, in-gel digested and identified by nano-HPLC-ESI-MS/MS. A total of 41 proteins were identified with high levels of confidence. Ten of these were significantly differentially expressed between the peritoneal dialysate samples before/after peritonitis. CONCLUSION: The present study was designed to establish optimal techniques to develop a proteomic map of peritoneal dialysate proteins. These proteins may not be new biomarkers; however, they may indicate a situation for possible drug treatment and can be the predictors of peritonitis for the validation study in the further.
Authors: Rebecca Herzog; Michael Boehm; Markus Unterwurzacher; Anja Wagner; Katja Parapatics; Peter Májek; André C Mueller; Anton Lichtenauer; Keiryn L Bennett; Seth L Alper; Andreas Vychytil; Christoph Aufricht; Klaus Kratochwill Journal: Mol Cell Proteomics Date: 2017-12-04 Impact factor: 5.911
Authors: Elisabete Oliveira; José E Araújo; Silvana Gómez-Meire; Carlos Lodeiro; Cristina Perez-Melon; Elena Iglesias-Lamas; Alfonso Otero-Glez; José L Capelo; Hugo M Santos Journal: Clin Proteomics Date: 2014-04-17 Impact factor: 3.988
Authors: Evelina Ferrantelli; Karima Farhat; Agnes L Hipgrave Ederveen; Karli R Reiding; Robert H J Beelen; Frans J van Ittersum; Manfred Wuhrer; Viktoria Dotz Journal: Sci Rep Date: 2018-01-17 Impact factor: 4.379
Authors: Mario Bonomini; Francesc E Borras; Maribel Troya-Saborido; Laura Carreras-Planella; Lorenzo Di Liberato; Arduino Arduini Journal: Int J Mol Sci Date: 2020-07-31 Impact factor: 5.923