Tubulointerstitial fibrosis in chronic renal disease is strongly associated with progressive loss of renal function. We studied the potential involvement of lysophosphatidic acid (LPA), a growth factor–like phospholipid, and its receptors LPA_1–4 in the development of tubulointerstitial fibrosis (TIF). Renal fibrosis was induced in mice by unilateral ureteral obstruction (UUO) for up to 8 d, and kidney explants were prepared from the distal poles to measure LPA release into conditioned media. After obstruction, the extracellular release of LPA increased approximately 3-fold. Real-time reverse transcription PCR (RT-PCR) analysis demonstrated significant upregulation in the expression of the LPA₁ receptor subtype, downregulation of LPA₃, and no change of LPA₂ or LPA₄. TIF was significantly attenuated in LPA₁ (−/−) mice compared to wild-type littermates, as measured by expression of collagen III, α-smooth muscle actin (α-SMA), and F4/80. Furthermore, treatment of wild-type mice with the LPA₁ antagonist Ki16425 similarly reduced fibrosis and significantly attenuated renal expression of the profibrotic cytokines connective tissue growth factor (CTGF) and transforming growth factor β (TGFβ). In vitro, LPA induced a rapid, dose-dependent increase in CTGF expression that was inhibited by Ki16425. In conclusion, LPA, likely acting through LPA₁, is involved in obstruction-induced TIF. Therefore, the LPA₁ receptor might be a pharmaceutical target to treat renal fibrosis.

The quest for the ideal therapeutic target in chronic kidney disease (CKD) has been riddled with many obstacles stemming from the molecular complexity of the disease and its co-morbidities. Recent advances in omics technologies and the resulting amount of available data encompassing genomics, proteomics, peptidomics, transcriptomics and metabolomics has created an opportunity for integrating omics datasets to build a comprehensive and dynamic model of the molecular changes in CKD for the purpose of biomarker and drug discovery. This article reviews relevant concepts in omics data integration using systems biology, a mathematical modelling method that globally describes a biological system on the basis of its modules and the functional connections that govern their behaviour. The review describes key databases and bioinformatics tools, as well as the challenges and limitations of the current state of the art, along with practical application to CKD therapeutic target discovery. Moreover, it describes how systems biology and visualization tools can be used to generate clinically relevant molecular models with the capability to identify specific disease pathways, recognize key events in disease development and track disease progression.

Peptides found in fetal urine predict end-stage renal disease in patients with a congenital abnormality of the kidney and urinary tract.

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m²; n = 10) or advanced (8.9±4.5 mL/min/1.73 m²; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = −0.8031; p<0.0001 and ρ = −0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = −0.6557; p = 0.0001 and ρ = −0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = −0.7752; p<0.0001 and ρ = −0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.