Introduction
Kidney transplantation is preferred over hemodialysis for end-stage renal disease (ESRD) patients. While current immunosuppressants inhibit T cell-mediated rejection, antibody-mediated rejection (AMR) caused by donor-specific antibodies against human leukocyte antigen (HLA) is becoming the major cause of graft loss after kidney transplantation. Treatment options for AMR are limited, and there is a need to identify factors that affect its occurrence and development. The gut microbiota is a complex microbial ecosystem that plays a vital role in maintaining host physiology and homeostasis, and alteration in its composition has been associated with various diseases. In a previous study, gut microbiota alteration was associated with AMR in kidney transplant recipients. This study aims to explore the shifts in intestinal metabolic profile among kidney transplantation recipients with AMR and identify the possible involvement of intestinal metabolism in regulating graft rejection.
Methods
This study enrolled 86 individuals from Henan Provincial People’s Hospital, including 30 kidney transplantation recipients with AMR, 35 with stable renal function, and 21 with ESRD. Fecal samples were collected from all participants, with those with AMR providing samples before treatment. Metabolites were extracted and analyzed using LC-MS, and statistical analyses were performed using XCMS, SIMCA-P, and SPSS software. Demographic and clinical data were also collected and analyzed using SPSS. Differential metabolites were identified based on a P value <0.05 and a VIP > 1.
Results
Differences in Intestinal Metabolic Profiles Among the KT-AMR, KT-SRF, and ESRD Groups
An untargeted metabolomics analysis was conducted on fecal samples from KT-AMR, KT-SRF, and ESRD groups using LC-MS to understand the intestinal metabolic changes associated with AMR after kidney transplant. The study identified significant discriminant metabolites using unsupervised principal component analysis (PCA) and supervised orthogonal partial least-squares discriminant analysis (OPLS-DA). The results showed that the intestinal metabolome of recipients with AMR differed significantly from those with ESRD, while they were not obviously different from those of recipients with stable renal functions. The study provides important clues for developing effective diagnostic biomarkers and therapeutic targets for AMR after kidney transplantation.
Identification of Differential Intestinal Metabolites
Metabolomics analysis identified potential metabolic biomarkers for AMR after kidney transplant. A total of 172 metabolites showed significant differences in the KT-AMR group compared to the ESRD group, while 25 metabolites were significantly different in the KT-AMR group compared to the KT-SRF group. Among the differential metabolites, 14 were differentially expressed in both the ESRD and KT-SRF groups compared to the KT-AMR group. N-Palmitoylsphingosine and Erucamide were higher, while 3b-Hydroxy-5-cholenoic acid, N-Acetyl-L-Histidine, Enoxolone, and Arg-Glu were lower in the KT-AMR group than the other groups (Figure 2).
Evaluation of the Discriminating Ability of Potential Biomarkers in AMR After Kidney Transplantation
The study conducted ROC curve analysis to determine if the differential metabolites could be used as a biomarker to differentiate between recipients with AMR, recipients with stable renal function, and patients with ESRD. The results showed that all 14 differential metabolites had AUC values larger than 0.7 when distinguishing recipients with AMR from patients with ESRD. The six top-ranked metabolites were able to discriminate between the KT-AMR and KT-SRF groups, with an AUC of 0.919. Moreover, Methylguanidine, Erucamide, 16-Hydroxypalmitic acid, and (S)-2-aminobutyric acid showed good discriminative power to distinguish the KT-SRF and ESRD groups.
Metabolic Pathway Enrichment Analysis
The study conducted KEGG analysis to identify functional pathways related to the differential metabolites between the three groups. The top 10 enriched pathways were represented by bubble charts. A total of 33 KEGG pathways were enriched with the 172 differential metabolites between the KT-AMR and ESRD groups, while 36 pathways were enriched with the 25 differential metabolites between the KT-AMR and KT-SRF groups. 20 pathways were shared between the pairwise comparisons. Pathway enrichment analysis was also performed for the differential metabolites between the KT-SRF and ESRD groups, and 34 enriched pathways were identified. These pathways mainly included ABC transporters, biosynthesis of amino acids, Histidine metabolism, and glutamate metabolism, among others.
Conclusion
To sum up, we compared the intestinal metabolic profiles of the KT-AMR group with those of the ESRD and KT-SRF groups, and discovered metabolites that are differentially expressed in AMR after kidney transplantation. Our results may offer crucial insights into developing useful diagnostic biomarkers and treatment targets for AMR after kidney transplantation. However, additional research is required to understand the impact of changes in intestinal metabolic profiles on the pathogenesis and progression of AMR.
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