Metabolomic and proteomic analyses of renal function after liver transplantation

Abstract

Background: Renal dysfunction is a common and serious complication in patients with end-stage liver diseases. While some patients recover renal function after liver transplantation (LT), others do not. Additionally, patients with normal kidney function (Normal-KF) before LT may develop post-transplant renal dysfunction. Early identification of patients at risk for impaired kidney function (Impaired-KF) post-LT is critical to improving outcomes. This study integrated metabolomic and proteomic analyses to investigate molecular profiles distinguishing Normal-KF from Impaired-KF post-LT.

Methods: Nine LT recipients were classified into Normal-KF (n = 5) and Impaired-KF (n = 4) groups. One additional recipient with pre-transplant renal function impairment who recovered renal function after LT, was analyzed separately. Serum samples were collected at 2- and 5-weeks post-LT. The metabolomic and proteomic profiles were assessed by untargeted liquid chromatography-tandem mass spectrometry.

Results: Metabolomic analysis identified 29 significantly altered metabolites between Normal-KF and Impaired-KF (fold change > 2, p < 0.05). Proteomic analysis revealed 45 differentially expressed proteins (fold change > 1.25, p < 0.05). For the recovered patient, the metabolomic profile closely resembled Normal-KF, whereas the proteomic profile remained aligned with Impaired-KF at both 14- and 35-days post-LT. From week 2 to week 5, both the metabolomic and proteomic profiles of the recovered patient showed trends toward the Normal-KF.

Conclusion: This study revealed distinct metabolomic and proteomic signatures associated with renal dysfunction post-LT. Proteomic profiles indicated a delayed recovery compared to metabolomic profiles, suggesting a dynamic and muti-layered renal recovery process. Further research is warranted to elucidate the functional implications of the differential proteins and metabolites for improved monitoring and therapeutic strategies.

Keywords: liver transplantation; mass spectrometry; metabolomics; proteomics; renal dysfunction.

Figure 1

Clustering analysis of metabolites and proteins. (A,C) Principal component analysis (PCA) with 108 metabolites (A) or 174 proteins (C), with colored regions representing 95% confidence intervals. (B,D) Hierarchical clustering analysis with 108 metabolites (B) or 174 proteins (D). Normal-KF, normal kidney function; Impaired-KF, impaired kidney function.