| Literature DB >> 35628461 |
Chiara Trincianti1, Vincenzo Meleca1, Edoardo La Porta2, Maurizio Bruschi3, Giovanni Candiano3, Andrea Garbarino3, Xhuliana Kajana3, Alberto Preda4, Francesca Lugani2, Gian Marco Ghiggeri2, Andrea Angeletti2, Pasquale Esposito5, Enrico Verrina6.
Abstract
Peritoneal dialysis (PD) represents the dialysis modality of choice for pediatric patients with end-stage kidney disease. Indeed, compared with hemodialysis (HD), it offers many advantages, including more flexibility, reduction of the risk of hospital-acquired infections, preservation of residual kidney function, and a better quality of life. However, despite these positive aspects, PD may be associated with several long-term complications that may impair both patient's general health and PD adequacy. In this view, chronic inflammation, caused by different factors, has a detrimental impact on the structure and function of the peritoneal membrane, leading to sclerosis and consequent PD failure both in adults and children. Although several studies investigated the complex pathogenic pathways underlying peritoneal membrane alterations, these processes remain still to explore. Understanding these mechanisms may provide novel approaches to improve the clinical outcome of pediatric PD patients through the identification of subjects at high risk of complications and the implementation of personalized interventions. In this review, we discuss the main experimental and clinical experiences exploring the potentiality of the proteomic analysis of peritoneal fluids and extracellular vesicles as a source of novel biomarkers in pediatric peritoneal dialysis.Entities:
Keywords: exosomes; extracellular vesicles; inflammation; mesothelial cells; pediatric peritoneal dialysis; proteomics
Mesh:
Substances:
Year: 2022 PMID: 35628461 PMCID: PMC9144397 DOI: 10.3390/ijms23105655
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 6.208
Figure 1Epithelial to mesenchymal transition of peritoneal membrane. A schematic representation of the peritoneal membrane composed of mesothelial cells monolayer and extracellular matrix within vessels and fibroblast and the progressive change of the structure due to epithelial-to-mesenchymal (EMT) transition. Moreover, the main markers that characterize the phenotype switch of mesothelial cells to fibroblast-like phenotype due to EMT are represented and reported. In the mirror below on the left, the most significant intracellular pathways of the TGF-β signaling are represented: (1) Dimeric TGF-β receptor type I and II before binding the ligand. (2) TGF-β binds receptors and, through phosphorylation, activates SMADs and Shad anchor for receptor activation (SARA) complexes. (TGFβ = transforming growth factor 1-β, αSMA = α smooth muscle actin).
Potential peritoneal-derived biomarkers in pediatric PD patients.
| Biomarkers | Main Findings | Reference |
|---|---|---|
| ANXA13 | Most significant potential biomarker in detecting peritoneal dialysis effluent exosomes of FSGS from No FSGS patients | [ |
| TIMP1 | Down-regulated protein in FSGS | [ |
| PTP4A1 | PD vintage and decreased PM function | [ |
| CENP-E, FCN2 | Up-regulated proteins in FSGS | [ |
| Caspase-3, IL-6, ZO-1 | Lumen narrowing of parietal peritoneal arterioles of patients exposed to high-GDP | [ |
| C1q and C3d | Abundance in PD-associated glucose exposure; correlation with the degree of arteriolopathy and high level of p-SMAD2/3 | [ |
| p-SMAD2/3 | Microvasculature damage mechanisms of the peritoneum vessels | [ |
| Intelectin-1 | Inflammation and fibrosis | [ |
| Defensive role against intestinal bacterial permeation and against a parasite | [ | |
| Cystatin C and B2M | Peritoneal dialysis efficiency | [ |
| Paraoxonase | Protection against toxic oxidative modification; possible correlation with early atherosclerotic changes in peritoneal dialysis | [ |
| Gelsolin | Protective role in mesothelial cell damage against infection | [ |
Abbreviations: Annexin 13 (ANXA13), Inhibitor matrix metalloproteinase 1 (TIMP1), Focal segmental glomerulosclerosis (FSGS), peritoneal membrane (PM), PTP4A1 (Protein Tyrosine Phosphatase 4A1), Ficolin-2 (FCN2), Centromere-associated protein E (CENP-E), interleukin-6 (IL-6), peritoneal dialysis (PD), glucose degradation products (GDP), Phosphorylated-suppressor of mothers against decapentaplegic 2/3 (p-SMAD2/3), Beta2-microglobulin (B2M).
Figure 2Isolation and characterization of mesothelial exosomes. Steps of the isolation and characterization of mesothelial exosome from peritoneal dialysis effluent: (A) Aliquots underwent a series of centrifugation cycles to remove mitochondria, other organelles, and microvesicles to obtain a purified fraction of exosomes. (B) Enriched exosomes fractions were mixed with polyclonal biotin-conjugated anti-human mesothelin. (C) Exosome size was determined by Dynamic Light Scattering, revealing a Gaussian distribution profile with a typical mean peak at 100 ± 5 nm (D) Western blot analysis revealed that the exosomes of both groups were positive for mesothelin (MSLN), CD81, and CD63, but not for CD45 (FSGS = focal segmental glomerular sclerosis).
Studies published on proteomic analysis in PD in children and proteomic analysis on PDE-derived EVs.
| Study | Population/Patients and Study Design | Main Topics/Research Aim | Main Biomarkers | Proteomic Analysis | AI Methods and Approaches | Ref. |
|---|---|---|---|---|---|---|
| Fang J. et al. | ADULTS | Proteomic analysis on PDE-EVs to identify potential biomarkers related to different Peritoneal membrane phenotypes | Glycoprotein 96 (GP96) | Liquid chromatography-tandem mass spectrometry. Mass spectrometer (Q Exactive HFX) coupled with nanoLC1200 system | Spectronaut 12.0 Pulsar (Biognosys). Data-dependent acquisition) technology. Blast2Go software was applied to associate Gene Ontology (GO) terms with the differentially expressed proteins | [ |
| Bruschi M. et al. | PEDIATRIC | Comparative proteomic analysis of mesothelial exosomes from PDE | Annexin A13 (ANXA13), | Orbitrap Fusion Tribrid mass spectrometer (ThermoScientific) | Andromeda engine, incorporated into MaxQuant software, was used to search spectra against Uniprot human database. | [ |
| Bartosova M. et al. | PEDIATRIC | Impact of GDP on vasculopathy of children in chronic peritoneal dialysis. Microdissected arterioles were isolated for transcriptome and proteome analysis ( | Caspase-3, TGF-β-induced-pSMAD2/3 interleukin-6, zonula occludens-1 (ZO-1) | Liquid chromatography–mass spectrometry (QExactive. Thermo Fisher) | Data were submitted to PRIDE (Proteomics Identification Database). Ingenuity Pathway Analysis software (Qiagen, Hilden, Germany). Similarity data (edges representing shared genes) were generated using R and visualized using Cytoscape 3.8.0 | [ |
| Carreras Planella L. et al. | ADULTS | To outline the | Endoglin, Thy-1 membrane glycoprotein (THY-1 or CD90) and biglycan (BGN), kininogen-1 (KNG1) | Liquid chromatography–mass spectrometry (LC-MS) (VelosOrbitrap-Thermo Fisher Scientific, Carlsbad, CA, USA) | Data were analyzed using Progenesis QI for proteomics software v3.0 (Nonlinear dynamics, Newcastle upon Tyne, UK). Peak lists generated s were analyzed with the Mascot search engine (v2.2, Matrix Science, London, UK). Protein identification was performed using the SwissProt-human database. Protein enrichment analysis was performed using Gene set enrichment analysis software (GSEA v3.0, Broad Institute, Cambridge, MA, USA) | [ |
| Bartosova M. et al. | PEDIATRIC | Multi-omic analysis to understand the mechanisms of CKD-associated arteriopathy. | C1q and C3d (terminal complement complex), pSMAD2/3 | LC-MS. Q Exactive mass spectrometer (Thermo Fisher Scientific, Carlsbad, CA, USA) | Data were processed and searched against the human SwissProt database with Andromeda search engine using MaxQuant. | [ |
| Pearson LJ. et al. | ADULTS | To demonstrate EVs in PDE and to characterize the related proteome | Mesothelin and cancer cell antigen 125 (MUC16). | LC-MS. mass spectrometer | Raw data were searched by X! Tandem (CYCLONE, 2013.2.01) against human databases (ENSEMBL v.76 Homo sapiens GRCh38). | [ |
| Carreras-Planella L. et al. | ADULTS | Identifying, isolating, and characterizing peritoneal dialysis efflux-extravesicles of patients on PD | CD9 | LC-MS/MS on a LTQ Orbitrap Velos (Thermo Fisher, Carlsbad, CA, USA). | Data were analyzed with Max Quant software against Uniprot human database. | [ |
| Bruschi M. et al. | PEDIATRIC | Proteomic characterization of PDE samples collected in patients with different APD treatment by the combined use of Combinatorial Peptide Ligand Library (CPLL) technology and two-dimensional electrophoresis | Gelsolin, intelectin-1 | Matrix-assisted | PDQuest Advance software for 2-DE experiments and QuantyOne software (Bio-Rad, Hercules, CA, USA) for western blot experiments. | [ |
| Bruschi M. et al. 2011 | PEDIATRIC | Proteome profile of PDE obtained with icodextrin or glucose-based solutions | β2-microglobulin cystatin C | LTQ linear ion trap mass spectrometer (Thermo Electron, San Jose, CA, USA) coupled to an HPLC Surveyor (Thermo Electron) | Protein identification was performed using SEQUEST software (Thermo Electron, | [ |
| Raaijmakers R. et al. 2008 | PEDIATRIC | To obtain the first representative overview of the proteome of PDE. | gelsolin | cyclotron resonance mass spectrometer (LIT FT-ICR MS) | Data were searched against the NCBI database using the Mascot search program. | [ |
Abbreviations: Artificial intelligence (AI); Peritoneal dialysis (PD); Automatic PD (APD), Peritoneal dialysis effluent (PDE); extracellular vesicles (EV); glucose degradation products (GDP); peritoneal equilibration test (PET); focal segmental glomerulosclerosis (FSGS): chronic kidney disease (CKD).