Application of metabolomics in urolithiasis: the discovery and usage of succinate

Abstract

Urinary stone is conceptualized as a chronic metabolic disorder punctuated by symptomatic stone events. It has been shown that the occurrence of calcium oxalate monohydrate (COM) during stone formation is regulated by crystal growth modifiers. Although crystallization inhibitors have been recognized as a therapeutic modality for decades, limited progress has been made in the discovery of effective modifiers to intervene with stone disease. In this study, we have used metabolomics technologies, a powerful approach to identify biomarkers by screening the urine components of the dynamic progression in a bladder stone model. By in-depth mining and analysis of metabolomics data, we have screened five differential metabolites. Through density functional theory studies and bulk crystallization, we found that three of them (salicyluric, gentisic acid and succinate) could effectively inhibit nucleation in vitro. We thereby assessed the impact of the inhibitors with an EG-induced rat model for kidney stones. Notably, succinate, a key player in the tricarboxylic acid cycle, could decrease kidney calcium deposition and injury in the model. Transcriptomic analysis further showed that the protective effect of succinate was mainly through anti-inflammation, inhibition of cell adhesion and osteogenic differentiation. These findings indicated that succinate may provide a new therapeutic option for urinary stones.

Fig. 1

The dynamic changes of stone and component analysis. a, b Representative images of the bladder by abdomen ultrasound of normal control and bladder stone model at 2 and 4 weeks. c Gross view and abdomen ultrasound of the stone sample from the same individual. d Infrared spectra and gross morphology of the stone. e, f Quantification of the calcium level in the urine and blood samples. *P < 0.05 compared with control group. For (e–f), n = 10 rabbits in each group

Fig. 2

Metabolomics profiling has identified five organic molecules as the candidate for crystal inhibitors. a Schematic representing the design of metabolomics profiling experiments. b Principal coordinate analysis (PCoA) for the positive and negative ions of the control and model animal based on metabolite profiles of the urine samples. c KEGG pathway enrichment analysis of the control and model groups. d Heatmap of ABC transporters, lysine degradation, phenylalanine metabolism and tyrosine metabolism pathway. e Alteration of the five organic molecules based on metabolite profiles and statistical analysis, *P < 0.05 compared with control group. For (a–e), n = 10 rabbits in each group

Fig. 3

Binding energy of the modifiers on the COM surface and Ca²⁺. a The optimized structures of COM, taurine, suberic acid, succinic, salicyluric and gentisic acids. b, c Optimized structures of the molecules on the COM (100) and COM (021) surface, with the light-pink, light-blue, brown, blue, yellow and red balls denoting the H, Ca, C, N, S and O atoms, respectively. d X-ray photoelectron spectra (XPS) of taurine, suberic acid, succinic, salicyluric and gentisic acids adsorbed to Ca²⁺. The fitted peaks in blue, pink and red correspond to the C1s peaks of C = O, C–O and C–C, respectively

Fig. 4

Inhibition of COM nucleation in vitro. a Optical (scale bar: 20 μm) and SEM (scale bar: 2 μm & 4 μm) micrographs. b–d Semi-quantitative analysis of the crystalized area and the number and ratio of COM and COD (COM—CaOx monohydrate, COD—CaOx dihydrate, n.d.—not defined), *P < 0.05 compared with the control group. e AFM images of (001) surface growth and inhibition with or without the modifier (scale bar: 400 nm). f FTIR spectra with or without the modifier

Fig. 5

Succinate has reduced kidney crystal deposition and injury in the EG-treated rat CaOx crystallization model. a Gross appearance of the kidneys. b, c Body and kidney weight, *P < 0.05 compared with the control group; # P < 0.05 compared with the model group. d Micro-CT imaging, e, f Sections of rat kidney were stained for crystal deposition by Von Kossa staining (scale bar: 400 μm), and the dark areas of the crystal were quantified, # P < 0.05 compared with the model group. g, h Plasma blood urea nitrogen (BUN) and creatinine (CREA), *P < 0.05 compared with the control group; # P < 0.05 compared with the model group

Fig. 6

Reversal of EG-induced kidney damage by succinate as illustrated by RNA sequencing. a, b Gene ontology (GO) term enrichment analysis of the biological processes influenced by succinate and EG-treatment based on RNA-seq dataset. Up- (left) and downregulated processes (right) in the succinate treated group as compared with EG-treated model group. c Dot plot showing pairwise GSEA pathway comparison of the RNA-seq dataset of the succinate- and EG-treated model group and the corresponding controls. Blue and red dots respectively represented the down- and upregulated pathways. d A scatter plot showing expression fold change of DEGs between EG-treated model group and succinate group based on the RNA-seq dataset. e Heatmaps showing the expression of genes associated with inflammatory, apoptotic, and survival events in the rat kidney tissues from the EG-treated and succinate treated groups based on RNA-seq analysis. For (a–e), n = 3 rats in each group