MO701: Origin of Proteins in Peritoneal Dialysis Explained by a Transcriptomics/Proteomics Cross-Over Analysis

Abstract

Abstract BACKGROUND AND AIMS Peritoneal dialysis effluent (PDE) is a rich but underexplored source of molecular markers for therapy monitoring and investigation of deregulated processes during PD. Modern high performance mass spectrometry (MS) and sequencing methods allow monitoring of hundreds of analytes in parallel. For understanding PD related processes on a systems biology level, a multilevel omics approach is particularly attractive. Here, we investigate the cellular transcriptome and cell-free proteome of PDE samples in combination with the publicly available Human Plasma Proteome Database to investigate the origin of proteins found in peritoneal dialysis effluent. METHOD Samples were obtained from clinically stable patients on chronic peritoneal dialysis during a highly standardized clinical routine check. The effluent material was separated into a cellular and cell-free component. Soluble proteins in the cell-free compartment were processed using our equalizing and TMT-labeling workflow followed by LC-MS. The cellular material was subjected to RNA sequencing. The Human Plasma Proteome database (peptideatlas.org/hupo/hppp) was used for referencing plasma proteins and estimating plasma concentration. A bioinformatic workflow conjoined information from the datasets to reveal novel insights into the ‘PD effluentome,’ especially clarifying the source of proteins found in PDE. RESULTS Combining two targeted metabolomics methods enabled detecting 207 unique metabolites in cell-free PDE. Metabolites not detected in our samples were included in the panel for in vitro studies of cellular systems. A mixed-effect ANOVA of all metabolites demonstrated dwell time-dependent concentration changes in 173 metabolites. Post-hoc testing revealed most metabolites to be changed between 1 and 16 h [ON] of fluid dwell (160), followed by 114 and 46 differently concentrated metabolites between 4 and 16 h and 1 and 4 h of dwell, respectively. We quantified 9797 transcripts in PD-effluent cells and 2729 solved proteins in PD effluent. A total of 342 proteins were filtered from plasma, while 800 proteins were attributable to local production. A quantitative analysis of the interaction proteome and cellular transcripts of roughly 1700 protein-transcript pairs showed clusters of proteins explained by overexpression in peritoneal cells compared to plasma concentrations. CONCLUSION Multi-omic profiling of PD effluent proved to be a valuable approach for revealing small molecule related changes during PD treatment. The exploitation of PD effluent information on multiple omics levels as identified by our bioinformatic approach has been shown to improve our understanding of the molecular processes in the peritoneal cavity and their role in development of complications for ultimately improving PD therapy. The combinatorial investigation of proteome and transcriptome of PD effluent represents a first step in identifying locally produced proteins for further validation as biomarkers of peritoneal health in peritoneal dialysis patients. Proteins of plasma origin could be tested for their value as diagnostic tools in monitoring treatment success and protein transport over the peritoneal barrier. Our work suggests feasibility of multi-omics approaches to investigate cell-derived biomarkers for their involvement in pathomechanisms relevant in PD.