#1043 Metabolomics and proteomics study on kidney function post-liver transplantation
Authors: Grobe Nadja , Wang Xiaoling , Gorlich Nina , Fribourg Miguel , Levitsky Josh , Cravedi Paolo , Kotanko Peter 
Published: May 2024

Background and Aims

Hepatorenal syndrome (HRS) is a serious complication characterized by kidney dysfunction in patients with advanced liver disease. The pathophysiology of HRS is poorly understood and involves a combination of hemodynamic, circulatory, inflammatory, and hormonal factors. While some patients with HRS restore kidney function after liver transplantation (LT), others continue to have impaired kidney function. Understanding the mechanisms associated with persistent kidney dysfunction is essential for developing effective treatments and stratifying patients for combined liver-kidney transplantation versus LT only.

Method

We analyzed serum samples from 10 patients of the multicenter prospective study (INAID CTOT14). Five of them had no HRS and maintained an eGFR &gt; 50 ml/min post-LT. One patient with HRS recovered kidney function post-LT. Four patients with HRS failed to regain kidney function at one- and two-months post-LT (eGFR &lt; 30 ml/min). We categorized the five patients without HRS and one patient with recovered eGFR as Normal Kidney Function (NKF) group and the other four patients as Impaired Kidney Function (IKF) group.

Serum samples were collected at one (visit 1) and two months (visit 2) post-LT and stored at −80°C until analysis. Both untargeted metabolomics and proteomics analysis were conducted using quantitative liquid chromatography tandem mass spectroscopy (LC-MS/MS) on Agilent® Q-TOF 6546. Data was analyzed by Agilent® Qualitative Workflow 10, Agilent® Profinder 10, MetaboAnalyst (V6.0), and Spectrum Mill MS software.

Results

A total of 150 metabolites were identified by LC-MS/MS based metabolomics. Principal component analysis (PCA) did not show significant differences between visit 1 and visit 2 samples (Fig. 1A). Therefore, visit 1 and visit 2 samples were analyzed together. With 12 samples in NKF and 8 samples in IKF group, the PCA score plot showed distinct clustering of the two groups (Fig. 1B).

Volcano plot analysis was conducted to analyze metabolite differences between NKF and IKF. P-values were calculated using a Student's t-test, and differential metabolites were identified with a P-value cut-off of &lt; .05 and a fold change &gt; 2.0. The result revealed 37 downregulated and 13 upregulated metabolites when comparing NKF and IKF (Fig. 1C). Notably, creatinine was among the downregulated metabolites in the NKF group, validating the accuracy of our analysis.

Untargeted proteomics analysis was also performed on all 20 samples. Using a P-value cut-off of .05 and a fold change &gt;1.25, 43 proteins were found to be upregulated, and four proteins to be downregulated when comparing NKF to IKF. Fig. 2A lists the top 10 regulated proteins, with beta-2-microglobulin as the most significantly changed. Reactome® pathway analysis of these 47 proteins highlighted potential impacts on various biological pathways (Fig. 2B), providing potential valuable insights into the molecular alterations associated with kidney function post-LT.

Conclusion

Our comprehensive metabolomics and proteomics study characterized the molecular alterations associated with kidney function post-LT. The identified metabolites and proteins may provide valuable insights for understanding persistent kidney damage. Future research is warranted to confirm and correlate these findings with clinical outcomes, enhancing the applicability and significance of our results in the broader context of HRS and LT.