Optimized Metabolomic Approach to Identify Uremic Solutes in Plasma of Stage 3–4 Chronic Kidney Disease Patients

Abstract

BackgroundChronic kidney disease (CKD) is characterized by the progressive accumulation of various potential toxic solutes. Furthermore, uremic plasma is a complex mixture hampering accurate determination of uremic toxin levels and the identification of novel uremic solutes.MethodsIn this study, we applied 1H-nuclear magnetic resonance (NMR) spectroscopy, following three distinct deproteinization strategies, to determine differences in the plasma metabolic status of stage 3–4 CKD patients and healthy controls. Moreover, the human renal proximal tubule cell line (ciPTEC) was used to study the influence of newly indentified uremic solutes on renal phenotype and functionality.ResultsProtein removal via ultrafiltration and acetonitrile precipitation are complementary techniques and both are required to obtain a clear metabolome profile. This new approach, revealed that a total of 14 metabolites were elevated in uremic plasma. In addition to confirming the retention of several previously identified uremic toxins, including p-cresyl sulphate, two novel uremic retentions solutes were detected, namely dimethyl sulphone (DMSO2) and 2-hydroxyisobutyric acid (2-HIBA). Our results show that these metabolites accumulate in non-dialysis CKD patients from 9±7 µM (control) to 51±29 µM and from 7 (0–9) µM (control) to 32±15 µM, respectively. Furthermore, exposure of ciPTEC to clinically relevant concentrations of both solutes resulted in an increased protein expression of the mesenchymal marker vimentin with more than 10% (p<0.05). Moreover, the loss of epithelial characteristics significantly correlated with a loss of glucuronidation activity (Pearson r = −0.63; p<0.05). In addition, both solutes did not affect cell viability nor mitochondrial activity.ConclusionsThis study demonstrates the importance of sample preparation techniques in the identification of uremic retention solutes using 1H-NMR spectroscopy, and provide insight into the negative impact of DMSO2 and 2-HIBA on ciPTEC, which could aid in understanding the progressive nature of renal disease.

Gut microbiome in chronic kidney disease: challenges and opportunities

“…Using metabolite profiling of plasma samples from 1,434 Framingham study participants, Rhee et al demonstrated that progression to CKD could be predicted using just 9 metabolites (100). Another metabolomic study applied NMR spectroscopy to determine differences in the plasma metabolic status of stage 3–4 CKD patients and healthy controls (101). This study revealed the elevation of 14 metabolites in the plasma of uremic patients, which included several previously identified uremic toxins, such as PCS, as well as two novel uremic retentions solutes, dimethyl sulphone and 2-hydroxyisobutyric acid (101).…”

“…Another metabolomic study applied NMR spectroscopy to determine differences in the plasma metabolic status of stage 3–4 CKD patients and healthy controls (101). This study revealed the elevation of 14 metabolites in the plasma of uremic patients, which included several previously identified uremic toxins, such as PCS, as well as two novel uremic retentions solutes, dimethyl sulphone and 2-hydroxyisobutyric acid (101). Ideally, to study uremic toxins, both proteomics and metabolomics approaches should be applied as these methods complement each other and each has a unique role in the discovery of uremic retention solutes.…”

Metabolomics and renal disease

“…Metabolomic surveys of earlier stages of CKD have provided insight on how metabolite alterations vary across levels of kidney function [16-19]. Duranton et al used a commercial LC-MS metabolomics vendor to measure amino acids and amino acid derivatives in plasma and urine from 52 individuals across different stages of CKD and plasma only from 25 individuals on dialysis [20].…”

Cordyceps sinensis protects against liver and heart injuries in a rat model of chronic kidney disease: a metabolomic analysis

“…Metabolomics is defined as the comprehensive and quantitative metabolic analysis of large numbers of endogenous, low-molecular-weight metabolites. In the past decade, the widespread utility of this analytical approach to monitoring multiple metabolic perturbations in living systems within pathological processes [13][14][15][16][17][18][19] , including CKD [17] , and the response to such external stimuli as drug treatments [20,21] has been illustrated. Moreover, this method is now being applied to the field of TCM [22][23][24][25][26] , as it could characterize the subtle metabolic interventions induced by TCM in the treatment of diseases and identify the potential implications of TCM.…”