Renal failure · 2026
Immunometabolic transcriptomic signatures of diabetes-related genes predict kidney transplant rejection.
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Casale Roberto, Dello Strologo Andrea, Alfieri Carlo, Grandaliano Giuseppe, Pesce Francesco
Аннотация
OBJECTIVES: Kidney transplantation remains the gold standard for end-stage renal disease, yet long-term graft survival is limited by acute and chronic rejection. Emerging evidence highlights the critical role of immunometabolism, the bidirectional relationship between alloimmunity and metabolic dysregulation, specifically in the context of post-transplant diabetes mellitus (PTDM). This study investigates whether the expression of genes traditionally associated with diabetes susceptibility can predict kidney transplant rejection.
METHODS: We performed a transcriptomic reanalysis of a publicly available dataset (GSE49198) comprising 596 peripheral blood samples from kidney transplant recipients. A panel of 13 candidate genes (INSR, CAT, TNF, MMP2, TGFB1, VEGFA, IGF1, PPARG, PPARGC1A, HLA-DQB1, CTLA4, ABCA1, DPP4) was selected based on prior domain knowledge. Patients were stratified into a High-Risk group (Acute and Chronic Rejection, n = 95) and a Low-Risk group (Stable, Tolerant, Minimal Immunosuppression, n = 439). Statistical analysis included Mann-Whitney U tests with Bonferroni correction, Spearman's rank correlation, hierarchical clustering, and 3D Principal Component Analysis (PCA).
RESULTS: Three genes (PPARG, INSR, and DPP4) showed statistically significant differential expression between High-Risk and Low-Risk cohorts (p < 0.05, corrected). PPARG and INSR were upregulated in rejection, while DPP4 was downregulated. Inter-gene correlation analysis revealed low redundancy. 3D PCA revealed a topological distinction: Low-Risk patients formed a dense homeostatic cluster, whereas High-Risk patients exhibited vector space dispersion ('metabolic scattering').
CONCLUSIONS: We identified a distinct metabolic gene signature capable of predicting kidney transplant rejection. The downregulation of DPP4 with the upregulation of PPARG and INSR suggests that metabolic reprogramming and loss of homeostasis are hallmarks of immune-mediated graft failure.
METHODS: We performed a transcriptomic reanalysis of a publicly available dataset (GSE49198) comprising 596 peripheral blood samples from kidney transplant recipients. A panel of 13 candidate genes (INSR, CAT, TNF, MMP2, TGFB1, VEGFA, IGF1, PPARG, PPARGC1A, HLA-DQB1, CTLA4, ABCA1, DPP4) was selected based on prior domain knowledge. Patients were stratified into a High-Risk group (Acute and Chronic Rejection, n = 95) and a Low-Risk group (Stable, Tolerant, Minimal Immunosuppression, n = 439). Statistical analysis included Mann-Whitney U tests with Bonferroni correction, Spearman's rank correlation, hierarchical clustering, and 3D Principal Component Analysis (PCA).
RESULTS: Three genes (PPARG, INSR, and DPP4) showed statistically significant differential expression between High-Risk and Low-Risk cohorts (p < 0.05, corrected). PPARG and INSR were upregulated in rejection, while DPP4 was downregulated. Inter-gene correlation analysis revealed low redundancy. 3D PCA revealed a topological distinction: Low-Risk patients formed a dense homeostatic cluster, whereas High-Risk patients exhibited vector space dispersion ('metabolic scattering').
CONCLUSIONS: We identified a distinct metabolic gene signature capable of predicting kidney transplant rejection. The downregulation of DPP4 with the upregulation of PPARG and INSR suggests that metabolic reprogramming and loss of homeostasis are hallmarks of immune-mediated graft failure.