The clinical and systems -omics for the identification of the MOlecular DEterminants of established Chronic Kidney Disease (iMODE-CKD) EU research project will incorporate multi-disciplinary expertise in different fields of -omics research. Genomic, transcriptomic, proteomic, metabolomic, imaging and clinical studies from leading academic and industry partners will be merged and integrated through systems biology approaches to yield the biological meaning of all generated information. This project will establish an initial training network between young researchers toward the identification and validation of molecules involved in the progression of CKD. Additionally, researchers will acquire key perspectives over the translational research pipeline from biomarker and drug target discovery to clinical implementation (Fig. 1). 
In contrast to previous studies using hypothesis-driven approaches to study complex human diseases, the iMODE-CKD project relies on the use of a hypothesis-free approach, where several multi-omic studies and systems biology analyses will be performed in order to unravel and gain an in-depth understanding of CKD progression. 
The identification of the CKD molecular subtypes and further patient stratification based on molecular and clinical parameters will provide more detailed and reliable information about patients’ therapeutic response, hence enabling a rational drug design and therapy [2]. To attain this requirement, the project involves application of high-throughput next generation sequencing (NGS) technologies, which will enable the detection of splicing variants, mapping single nucleotide polymorphisms (SNPs) and will determine gene regulation. Additionally, data derived from transcriptomic studies, will allow the correlation of miRNAs from body fluids with those from kidney tissues and their respective mRNA targets. This will provide valuable insights for the establishment of miRNAs as potential biomarkers for diagnostic purposes and patient monitoring. 
The urinary proteome is broadly used for clinical diagnosis and prognosis of patients with kidney disease. However, metabolomics studies in relation to kidney disease are currently scarce [3]. To fulfil this demand, state-of-the-art proteomic profiling using fractionation techniques, such as capillary electrophoresis coupled to mass spectrometry (CE-MS), as well as depletion strategies to allow for the detection of low abundance proteins, will be applied to
study the abundance of proteins and peptides in body fluids.. 
The association of molecular, histological and clinical data can be achieved through the use of MALDI imaging mass spectrometry, an in situ proteomics technology that combines proteomics with conventional histology [5]. Application of this technique alloww for the identification of morphometric features within the sample and also molecular biomarkers that will be directly correlated with a certain clinical event/phenotype [3]. 
All aforementioned multidimensional -omics data can be used to construct models of
molecular interaction networks, using both prior and de novo knowledge, therefore linking genes with disease based on genome-wide association studies, miRNAs and mRNAs targets, protein-DNA interactions, protein-protein interactions, protein-substrate binding, metabolic pathway interactions and drug-target interactions (Fig. 1B and 1C) [4], where these molecular entities are represented as nodes and their interactions as edges [6]) The assembled
interaction networks can be clustered into modules of kidney disease and serve as sets of disease biomarkers and/or potential drug targets [4]. The assessment of these predicted targets will then be initially conducted in silico (Fig. 1D), resulting in a shorter list of candidates to be further confirmed experimentally in future studies [7].
Figure 1 General overview of CKD traits  
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2. Formentini I, Bobadilla M, Haefliger C, Hartmann G, Loghman-Adham M, et al. (2012) Current drug development challenges in chronic kidney disease (CKD)--identification of individualized determinants of renal progression and premature cardiovascular disease (CVD). Nephrol Dial Transplant 27 Suppl 3: iii81-88.
3. Jiang S, Chuang PY, Liu ZH, He JC (2013) The primary glomerulonephritides: A systems biology approach. Nature Reviews Nephrology 9: 500-512.
4. He JC, Chuang PY, Ma'Ayan A, Iyengar R (2012) Systems biology of kidney diseases. Kidney International 81: 22-39.
5. Mainini V, Pagni F, Ferrario F, Pieruzzi F, Grasso M, et al. (2014) MALDI imaging mass spectrometry in glomerulonephritis: Feasibility study. Histopathology 64: 901-906.
6. Mayer P, Mayer B, Mayer G (2012) Systems biology building a useful model from multiple markers and profiles. Nephrology Dialysis Transplantation 27: 3995-4002.
7. Terstappen GC, Reggiani A (2001) In silico research in drug discovery. Trends Pharmacol Sci 22: 23-26.
8. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HYK, et al. (2012) Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148: 1293-1307.












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