Metabolomics Profiling of Nephrotic Syndrome towards Biomarker Discovery

Abstract

Nephrotic syndrome (NS) is a kidney illness characterized by excessive proteinuria, hypoalbuminemia, edema, and hyperlipidemia, which may lead to kidney failure and necessitate renal transplantation. End-stage renal disease, cardiovascular issues, and mortality are much more common in those with NS. Therefore, the present study aimed to identify potential new biomarkers associated with the pathogenesis and diagnosis of NS. The liquid chromatography-mass spectrometry (LC-MS) metabolomics approach was applied to profile the metabolome of human serum of patients with NS. A total of 176 metabolites were significantly altered in NS compared to the control. Arginine, proline, and tryptophan metabolism; arginine, phenylalanine, tyrosine, and tryptophan biosynthesis were the most common metabolic pathways dysregulated in NS. Furthermore, alanyl-lysine and isoleucyl-threonine had the highest discrimination between NS and healthy groups. The candidate biomarkers may lead to understanding the possible metabolic alterations associated with NS and serve as potential diagnostic biomarkers.

Keywords: biomarker; liquid chromatography–mass spectrometry; metabolomics; nephrotic syndrome.

Figure 1

(A) Orthogonal partial least square discriminant analysis (OPLS-DA) score plot shows clear clustering and separation between patients with NS (n = 6) and healthy control (n = 33) representing the level of global metabolic differential expressions in NS compared to control. (B) The loading plot shows the regulation of metabolic expression of metabolites between NS and control.

Figure 2

Binary comparison for serum samples of patients with NS and healthy controls. (A) Dysregulated metabolites between NS vs. control with fold change cut-off of 2 and false discovery rate (FDR) threshold of 0.05; up-regulated = 105 (red), down-regulated = 71 (blue). (B) Hierarchal clustering (HAC) and heat map analysis of the top 25 significantly altered metabolites between the two study groups; controls (red), and NS (green).

Figure 3

Pathway analysis shows the altered pathways in NS including amino acid metabolism and ubiquinone biosynthesis. The node color and size of the circle reflect the p-value and the pathway impact value, respectively.

Figure 4

Receiver operating characteristics (ROC) curve and loading Plots for significant metabolites in serum of patients with NS. (A) ROC was generated using the PLS-DA model showing area under the curve (AUC) for the top five variants = 1. (B) Frequency percentage plot of the altered metabolites in NS patients when compared with controls. (C) Alanyl-Lysine (AUC = 1) and (D) Isoleucyl-Threonine (AUC = 1) were up-regulated in NS patients when compared with control.