Information for Researchers
The fields of research within the iMODE-CKD are:
Clinical data collection and patient phenotyping (ESR1 University of Skopje)
Description: The clinical data collected from ongoing ethically approved CKD clinical studies will be used to define relevant clinical parameters and to develop a unified database for CKD. Potential biomarkers for CKD progression will be evaluated within this context.
  • Clinical data collection.
  • Definition of relevant clinical core parameter set.
  • Unification of database systems from existing sample collections.
  • Assessment of different -omics biomarkers.


Existing data consolidation (ESR2 Biomedical Research Foundation of the Academy of Athens)
Description: Currently there is a wide array of publicly available datasets and literature resources related to CKD progression. The identification of associated clusters amongst these datasets will be integrated into a CKD progression database.
  • To extract and analyze available datasets, and key publications relevant to CKD progression.
  • To generate a CKD progression relevant database.
  • To perform initial statistical analysis of existing data for the identification of CKD progression associated clusters.
Tools: Semantics, Existing biological databases & MySQL, R, PHP & in-house scripts, Ingenuity Pathway Analysis (IPA).
Urine proteomics in relation to CKD progression (ESR3 Biomedical Research Foundation of the Academy of Athens)
Description: To reduce sample complexity and facilitate the identification of low abundance proteins, the urinary proteome will be fractionated using chromatographic techniques and analyzed via high resolution LC-MS/MS. The project will focus on the identification and validation of urinary proteins involved in CKD progression.
  • Identification of differentially expressed urinary proteins in relation to CKD progression.
  • Functional annotation of findings
  • Verification of differentially expressed proteins by immunological techniques
o Fractionation techniques: Immobilized metal affinity chromatography (IMAC), 2-dimensional electrophoresis, glycoprotein enrichment, abundant protein immunodepletion
  • Instrumentation: MALDI-TOF, LC-MS/MS (Orbitrap Velos)
  • Software: Trans-Proteomic Pipeline (TPP), Proteome Discoverer, Cytoscape, DAVID, Ingenuity Pathway Analysis (IPA).


Urinary peptidomics in relation to CKD progression (ESR4 Mosaiques Diagnostics GMBH)
Description: To investigate human urine samples using high resolution peptidomics techniques. Also, to identify and validate biomarkers that are specific for detecting CKD progression.
  • To analyze urine samples from the i-MODE CKD clinical study using CE-MS.
  • To identify biomarkers specific for detecting CKD progression.
  • To obtain sequences of potential biomarkers using CE- and LC-MS/MS.
  • To blindly validate biomarkers in patient cohorts.
Tools: CE-ESI-TOF, CE-MS/MS, LC-MS/MS analysis and bioinformatic tools for data evaluation ( Mosavisu, Mosacluster, Mosadiagnostics).
miRNA in CKD progression (ESR5 INSERM)
Description: Micro RNAs have been shown to have a high impact on gene post-transcriptional regulation, and thus a direct impact on protein translation. miRNAs are present in urine. The detection, validation and correlation of urine miRNAs with kidney tissue miRNAs, mRNAs, and proteins can be very informative for the establishment of miRNAs as potential biomarkers for CKD progression.
  • To study miRNA modifications in urine and tissue samples in CKD patients.
  • To validate differentially expressed miRNAs with qRT-PCR.
Tools: Microarrays, qRT-PCR, bioinformatics tools (IPA, miRBase).
Data integration to generate a molecular map of primary glomerulosclerosis (ESR6 University of Glasgow)
Description: The project will use integrative Systems Biology approaches and apply existing or emerging Bioinformatics tools, in order to yield a novel molecular map of established CKD.
Gathered data will be integrated as follows:
  • prediction of proteolytic events by integrating proteomics data from urine and tissue;
  • identification of miRNA targets through the correlation between transcriptomics and miRNA data;
  • generation of pathways of protein modification and degradation by the correlation of proteomics and metabolomics data in urine and plasma; and,
  • integration of molecular, imaging and clinical data.
These studies will ultimately promote the development of iterative models of molecular processes for different disease stages and will allow the prediction of key nodes, biomarkers, novel drug targets.
  • Statistical tools/software (Mainly R package and SPSS)
  • Data-mining (Agilent Literature Search plugin for Cytoscape, literature and databases)
  • Network analysis and representation (ClueGO, MetScape and MiMI plugins for Cytoscape)
  • Pathway analysis (PathVisio, KEGG, Reactome).


Plasma peptidomics for the identification of prognostic biomarkers for CKD (ESR7-University of Aachen)
• Description: Similar to urine peptidomics, the analysis of plasma peptidomics will elucidate post-translational modifications of peptides/proteins and their impact on uremic retention of solutes associated with CKD progression. This will be assessed using LC-ESI-MS, as well as MALDI MS.
  • To identify and quantify plasma peptides.
  • To investigate post-translational modifications of proteins /peptides.
  • To study the binding affinity of selected uremic retention solutes (such as indoxyl sulfate, p-cresyl sulfate and phenyl acetic acid) on modified proteins/peptides.
Tissue proteomics and imaging by mass spectrometry in established CKD (ESR8 University of Milano Bicocca)
Description: Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a unique proteomic technology that explores the spatial distribution of biomolecules directly in situ, thus integrating molecular and morphological information. Tissue proteomics of renal biopsies using MALDI-MSI, including cutting edge protocols for the analysis of both frozen and FFPE biopsies, will enable us to visualize the spatial distribution of biomolecules within renal tissue and to correlate results with histological findings. Integration with other -omics techniques will further enhance the detection and identification of potential biomarkers for CKD progression.
  • To visualize the spatial distribution of putative biomarker molecules within renal tissue.
  • To correlate tissue biomarkers with histological findings.
  • To identify tissue proteins and peptides associated with progression of primary glomerolunephritis.
Tools: Analysis will be performed using a Bruker ultrafleXtreme MALDI/TOF-TOF and analysis using ClinProTools and SCILS Lab.
Gene expression in relation to CKD progression (ESR9 ServiceXS)
Description: With the aid of recently developed NGS (Next Generation Sequencing) technologies, we will detect differences in gene expression, splicing variants, or variants (such as INDELs or SNPs) that will elucidate the mechanisms of CKD progression.
  • To identify transcripts associated with the progression of primary glomerulonephritis.
  • To detect possible splice variants and potential disease-associated mutations.
  • To independently validate newly discovered and disease-associated genomic and transciptomic changes..
  • To integrate the transcriptomics with of other –omics datasets in relation to CKD progression.
Tools: RNA sequencing (Proton P1, HiSeq 2500, NextSeq 500). High Throughput microarray (Gene Titan, Affymetrix U219). Bioinformatic tools. Independent qPCR validation.
Body fluids metabolomics in CKD progression (ESR10 Helmholtz Zentrum Munchen)
Description: This project involves the generation of a metabolic master map of plasma and urine during CKD progression using high performance chromatography in combination with ultrahigh resolution 12 Telsa FT-ICR-MS and selected NMR analysis. The scope is to identify and validate novel metabolomic biomarkers by elucidating their structures and demonstrating their functions relative to CKD. The validation of the methodology for metabolic biomarker detection, as well as the development of statistical tools for data analysis, will also be undertaken.
  • To develop a non-targeted approach for biomarker detection in urine and plasma of human CKD patients.
  • To integrate metabolomics with other –omics datasets for a holistic systems biology understanding of CKD.
  • To establish a potential model for the noninvasive diagnosis of CKD based on differentially expressed metabolites.
In conclusion:
The researchers associated with the iMODE-CKD ITN program aim to integrate their multidisciplinary expertise, cutting edge technologies, and specialized software to unravel the insights of CKD progression. In combination, all findings will provide the foundation for developing relevant molecular models underlying CKD progression hence revealing biology-
driven biomarkers and critical “nodes” (i.e. potential therapeutic targets) for CKD progression.
















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