Identification of biomarkers using-omics approach for the early detection of chronic kidney disease and its complications

Early diagnosis of diseases is a big challenge to improve patients' health and quality of life. 'Omics' analyses, which allow the global and simultaneous quantification of the relative abundance of thousands of molecules in biological fluids are promising for the identification of early biomarkers of complex diseases. In this context, the objective of my thesis was to develop diagnostic tools, based on urinary peptidome and metabolome analyses, for the early detection of chronic kidney disease (CKD) and associated cardiovascular complications. The first study, as part of the 4C European project (Cardiovascular Complications in Children with Chronic kidney disease), focused on analyzing the cardiovascular complications associated to CKD in children. These complications are the main cause of mortality in children with CKD and their early diagnosis is impossible for now. Analysis of the urinary peptidome of 86 children with or without cardiovascular complications associated to CKD by capillary electrophoresis coupled to mass spectrometry (CE-MS), led to the identification of two sets of peptides for the early prediction of high cardiovascular risk in pediatric patients: 190 peptides were associated to an increase of the carotid intima-media thickness (AUC 0.87, sensitivity 80%, specificity 100%) and 22 peptides were associated to an increase in arterial stiffness (AUC 0.83, sensitivity 83%, specificity 70%). The second study falls in the field of veterinary medicine. In this study, carried out on 50 dogs with or without CKD, we analyzed for the first time the canine urinary peptidome using the CE-MS technology. We identified 133 urinary peptides associated to CKD allowing an accurate diagnosis of CKD in 80% of the dogs. Metabolites correlate best to phenotype compared to other molecular traits. However, the use of metabolomics for identification of clinically relevant biomarkers is very limited due to the lack of high-performance analytical technologies. The third part of my thesis was to develop a procedure for the quantification of urinary metabolites by CE-MS. Using a unique method of internal normalization based on endogenous and stable metabolites, we can now analyze the metabolite content of the same urine sample with a high reproducibility over the long-term (4 years). As a proof-of-concept, we demonstrated that this developed procedure led to the identification of a set of 32 urinary metabolites that allow the early identification of newborns with an obstructive kidney anomaly with a sensitivity of 76% and a specificity of 86%. Finally, the fourth study was dedicated to improving translational research. The aim was to develop aptasensors able to detect 'omics'-identified biomarkers with a high affinity and specificity to obtain a simple, rapid and low-cost diagnostic test. The biomarker chosen as target is a urinary fragment of alpha-1-antitrypsin, which is ~1000 more abundant in adults with CKD compared to healthy subjects. Aptasensors were selected by the Systematic Evolution of Ligands by EXponential enrichment (SELEX). Here we present preliminary work on the development of the SELEX for our target. In conclusion, this thesis shows the strength of the urinary content, in terms of peptides and metabolites, for the early diagnosis of complex pathologies like CKD and the associated cardiovascular complications. Moreover, the selection of aptasensors targeting these early biomarkers and that can be used at bedside, will revolutionize future diagnostic methods.