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Ischemia reperfusion injury (IRI) is an inevitable process in renal transplantation, which is closely related to serious postoperative complications such as delayed graft function (DGF), acute rejection and graft failure. Neutrophil extracellular traps (NETs) are extracellular DNA structures decorated with various protein substances released by neutrophils under strong signal stimulation. Recently, NETs have been found to play an important role in the process of IRI. This study aimed to comprehensively analyze the expression landscape of NET-related genes (NRGs) during IRI, identify clusters with different degrees of IRI and construct robust DGF and long-term graft survival predictive strategies. The microarray and RNA-seq datasets were obtained from the GEO database. Differentially expressed NRGs (DE-NRGs) were identified by the differential expression analysis, and the NMF algorithm was used to conduct a cluster analysis of IRI samples. Machine learning algorithms were performed to screen DGF-related hub NRGs, and DGF and long-term graft survival predictive strategies were constructed based on these hub NRGs. Finally, we verified the expression of Cxcl1 and its effect on IRI and NETs generation in the mouse IRI model. This study revealed two IRI clusters (C1 and C2 clusters) with different molecular features and clinical characteristics. Cluster C1 was characterized by active metabolism, mild inflammation and lower incidence of DGF, while Cluster C2 was inflammation activated subtype with a higher incidence of DGF. Besides, based on DGF-related hub NRGs, we successfully constructed robust DGF and long-term graft survival predictive strategies. The mouse renal IRI model verified that Cxcl1 was significantly upregulated in renal tissues after IRI, and using a CXCL8/CXCL1 inhibitor could significantly improve renal function, alleviate renal tubular necrosis, tissue inflammatory response, and NET formation. This study identified two distinct IRI clusters based on DE-NRGs and constructed robust prediction methods for DGF and graft survival, which can provide references for early prevention and individualized treatment of various postoperative complications after renal transplantation.

New biomarkers of early and late graft dysfunction are needed in renal transplant to improve management of complications and prolong graft survival. A wide range of potential diagnostic and prognostic biomarkers, measured in different biological fluids (serum, plasma, urine) and in renal tissues, have been proposed for post-transplant delayed graft function (DGF), acute rejection (AR), and chronic allograft dysfunction (CAD). This review investigates old and new potential biomarkers for each of these clinical domains, seeking to underline their limits and strengths. OMICs technology has allowed identifying many candidate biomarkers, providing diagnostic and prognostic information at very early stages of pathological processes, such as AR. Donor-derived cell-free DNA (ddcfDNA) and extracellular vesicles (EVs) are further promising tools. Although most of these biomarkers still need to be validated in multiple independent cohorts and standardized, they are paving the way for substantial advances, such as the possibility of accurately predicting risk of DGF before graft is implanted, of making a “molecular” diagnosis of subclinical rejection even before histological lesions develop, or of dissecting etiology of CAD. Identification of “immunoquiescent” or even tolerant patients to guide minimization of immunosuppressive therapy is another area of active research. The parallel progress in imaging techniques, bioinformatics, and artificial intelligence (AI) is helping to fully exploit the wealth of information provided by biomarkers, leading to improved disease nosology of old entities such as transplant glomerulopathy. Prospective studies are needed to assess whether introduction of these new sets of biomarkers into clinical practice could actually reduce the need for renal biopsy, integrate traditional tools, and ultimately improve graft survival compared to current management.

The pathogenesis of allograft (dys)function has been increasingly studied using ‘omics’-based technologies, but the focus on individual organs has created knowledge gaps that neither unify nor distinguish pathological mechanisms across allografts. Here we present a comprehensive study of human pan-organ allograft dysfunction, analyzing 150 datasets with more than 12,000 samples across four commonly transplanted solid organs (heart, lung, liver and kidney, n  = 1,160, 1,241, 1,216 and 8,853 samples, respectively) that we leveraged to explore transcriptomic differences among allograft dysfunction (delayed graft function, acute rejection and fibrosis), tolerance and stable graft function. We identified genes that correlated robustly with allograft dysfunction across heart, lung, liver and kidney transplantation. Furthermore, we developed a transfer learning omics prediction framework that, by borrowing information across organs, demonstrated superior classifications compared to models trained on single organs. These findings were validated using a single-center prospective kidney transplant cohort study (a collective 329 samples across two timepoints), providing insights supporting the potential clinical utility of our approach. Our study establishes the capacity for machine learning models to learn across organs and presents a transcriptomic transplant resource that can be employed to develop pan-organ biomarkers of allograft dysfunction. A comprehensive analysis of omics data from biopsies and blood samples from more than 12,000 cases of heart, lung, liver and kidney transplants provides insights into shared mechanisms of allograft dysfunction across organs.

Background Ischemia reperfusion injury (IRI) in kidney transplantation (KTx) is closely associated with acute rejection, delayed graft function (DGF), and graft failure. Evidence highlights the significant correlation between mitochondrial dysfunction and IRI. However, there is a scarcity of predictive models for DGF and long-term graft survival specifically focused on mitochondrial-related genes (MGs). Methods RNA-seq and Microarray datasets from the GEO database were utilized. Differential expression analysis identified differentially expressed MGs (DE-MGs), and consensus clustering analysis performed cluster analysis of IRI samples. Comprehensive bioinformatics methods and machine learning were applied to establish predictive models for DGF and long-term graft survival based on DE-MGs. Additionally, scRNA-seq was used for further analysis of the DE-MGs. Results Our study identified two IRI clusters (C1 and C2) with distinct molecular features and clinical characteristics. C1 represented an inflammation and immune-activated subtype with a higher incidence of DGF, whereas C2 exhibited active metabolism and a lower DGF incidence. Moreover, utilizing DE-MGs, we developed reliable predictive models for DGF and long-term graft survival. Additionally, we observed that DE-MGs were predominantly expressed in mast cells and found their crucial role in the cell communication networks by activation of AREG-EGFR, CSF1-CSF1R, NAMPT-INSR, and CTSG-PARD3 receptor-ligand pairs in KTx. Conclusion This study identified two distinct IRI clusters and developed powerful prediction models for DGF and long-term graft survival using DE-MGs. Additionally, it highlighted the crucial role of mast cells in KTx. These findings have implications for early prevention and customized therapy of postoperative complications in KTx.

Ferroptotic waves aggravate kidney ischemia-reperfusion injury and drive delayed graft function (DGF). We demonstrate that elevated glycolysis and lactate production in graft kidney correlate with ferroptosis and functional impairment. A signaling axis composed of the long non-coding RNA IGIP-5, microRNA 670-3p, and lactate dehydrogenase A promotes lactate secretion from injured tubular cells, inducing lactylation and ferroptosis in neighboring cells and triggering ferroptotic waves. Lactylome profiling identifies that nucleophosmin 1 (NPM1), an epigenetic regulator, is lactylated at lysine 257 by the lactyltransferase AARS1. Suppressing NPM1 lactylation—via K257 mutation, AARS1 knockout, or taurochenodeoxycholic acid—upregulates SLC7A11 and inhibits ferroptosis. Mechanistically, lactylation stabilizes NPM1 by reducing MDM2-mediated ubiquitination and strengthens SLC7A11 repression, disrupting cystine metabolism. In mouse allografts, blocking lactate shuttle-mediated NPM1 lactylation prevents ferroptotic propagation and ameliorates graft function. Additionally, we develop an early prediction model for DGF using postoperative urinary lactate concentrations. These findings reveal a metabolic-epigenetic axis driving ferroptotic propagation and propose NPM1 lactylation as a therapeutic target for DGF. Ferroptotic propagation aggravates kidney ischemia-reperfusion injury and causes delayed graft function. Here, the authors show that inhibiting lactate shuttle and AARS1-mediated NPM1 lactylation protects graft function by blocking ferroptotic waves.

Assessment and treatment of severe ischemia-reperfusion-injury (IRI) remains an unmet challenge in kidney transplantation. Normothermic machine perfusion (NMP) recapitulates IRI , but there is limited understanding of the transcriptional pathways, and the associated cellular landscape, driving IRI during NMP and determining its severity. Such knowledge is essential for therapeutic targeting and organ resuscitation during machine perfusion. Using tissue obtained at the time of NMP from kidneys subsequently transplanted as part of a randomized controlled trial, we undertook in-depth transcriptomic analyses comparing kidneys suffering severe IRI, (manifesting clinically as the development of delayed graft function (DGF)), to kidneys with mild IRI (defined by immediate graft function, IGF) post-transplantation. We validated upregulation of previously described pro-inflammatory and immune transcriptomic pathways, including and . Going further, we identified innate immune system driven processes at the core of the transcriptional signature in kidneys suffering severe IRI, such as recruitment and migration of myeloid leucocytes, macrophage activation, phagocytosis and inflammasome activation. Deconvolution using single-cell-RNAseq data showed kidneys with severe IRI and post-transplant DGF were enriched for pro-inflammatory mononuclear phagocytes, myofibroblasts and fibroblasts, but depleted of tubuloepithelial, cell signatures. These transcriptional findings were recapitulated in tissue biopsies obtained during NMP from an external cohort comparing kidneys with high acute tubular injury and severe IRI to kidneys with low acute tubular injury and mild IRI; these kidneys were histologically similar to the DGF/IGF kidneys, respectively. Together, our study characterizes the transcriptional signature of severe IRI during NMP, suggesting the role of pro-inflammatory innate/pro-fibrotic cells in this process. We describe a transcriptomic signature that may support future prospective therapeutic trials as a potential efficacy endpoint, and highlight potential cellular targets for therapeutic intervention during NMP in an era of precision medicine.

Complement plays a central role in organ ischemia/reperfusion injury (IRI) and allograft rejection. A retrospective observational study included a cohort of 73 non-diabetic deceased donor kidney allograft recipients. We collected data on donor and recipient demographic, clinical and laboratory parameters. The main outcomes of our study were delayed graft function (DGF) and kidney allograft function during five years posttransplant. Gene single nucleotide polymorphisms (SNPs) for complement components C3 (rs2230199, G_C) and FH (rs800292, G_A) were determined. The genotyping results for FH polymorphism (184G > A) showed a distribution of GG (71.2%) and GA (28.8%) genotypes, with the AA genotype not detected in the cohort. The genotype frequencies of the C3 polymorphism (304 C > G) were CC (71.2%), CG (26.0%) and GG (2.8%). Analysis of FH SNP demonstrated that patients with the GG genotype had a statistically higher frequency of DGF compared to those with the GA genotype (67.3% vs. 38.1%, p  = 0.022). Univariate linear regression analysis confirmed that the FH GG genotype was the only significant determinant of DGF ( p  = 0.025). Analysis of C3 SNP showed that patients with the GC/GG genotype demonstrated significantly lower levels of creatinine clearance compared to those with the CC genotype at 1 year ( p  = 0.002), 3 years ( p  = 0.001) and 5 years ( p  = 0.010) posttransplant. These findings underscore the importance of genetic factors in influencing renal outcomes post-transplant.

Renal ischemia reperfusion (IR)-injury induces activation of innate immune response which sustains renal injury and contributes to the development of delayed graft function (DGF). Triggering receptor expressed on myeloid cells-1 (TREM-1) is a pro-inflammatory evolutionary conserved pattern recognition receptor expressed on a variety of innate immune cells. TREM-1 expression increases following acute and chronic renal injury. However, the function of TREM-1 in renal IR is still unclear. Here, we investigated expression and function of TREM-1 in a murine model of renal IR using different TREM-1 inhibitors: LP17, LR12 and TREM-1 fusion protein. In a human study, we analyzed the association of non-synonymous single nucleotide variants in the TREM1 gene in a cohort comprising 1263 matching donors and recipients with post-transplant outcomes, including DGF. Our findings demonstrated that, following murine IR, renal TREM-1 expression increased due to the influx of Trem1 mRNA expressing cells detected by in situ hybridization. However, TREM-1 interventions by means of LP17, LR12 and TREM-1 fusion protein did not ameliorate IR-induced injury. In the human renal transplant cohort, donor and recipient TREM1 gene variant p.Thr25Ser was not associated with DGF, nor with biopsy-proven rejection or death-censored graft failure. We conclude that TREM-1 does not play a major role during experimental renal IR and after kidney transplantation.

Delayed graft function (DGF) is one of the major obstacles for graft survival for kidney recipients. It is profound to reduce the incidence of DGF for maintaining long-term graft survival. However, the molecular regulation of DGF is still not adequately explained and the biomarkers for DGF are limited. Exosomes are cell-derived membrane vesicles, contents of which are stable and could be delivered into recipient cells to exert their biological functions. Consequently, exosome-derived proteomic and RNA signature profiles are often used to account for the molecular regulation of diseases or reflect the conditional state of their tissue as biomarkers. Few researches have been done to demonstrate the function of exosomes associated with DGF. In this study, high-throughput sequencing was used to explore the miRNA expression profiling of exosomes in the peripheral blood of kidney recipients with DGF. We identified 52 known and 5 conserved exosomal miRNAs specifically expressed in recipients with DGF. Three coexpressed miRNAs, hsa-miR-33a-5p_R-1, hsa-miR-98-5p, and hsa-miR-151a-5p, were observed to be significantly upregulated in kidney recipients with DGF. Moreover, hsa-miR-151a-5p was positively correlated with the first-week serum CR, BUN, and UA levels of the kidney recipients after transplantation. Furthermore, we also analyzed functions and signaling pathways of the three upregulated miRNAs target genes to uncover putative mechanism of how these exosomal miRNAs functioned in DGF. Overall, these findings identified biomarker candidates for DGF and provided new insights into the important role of the exosomal miRNAs regulation in DGF.

β-1,4-mannosylglycoprotein 4-β-N-acetylglucosaminyltransferase (MGAT3) is a key molecule for the innate immune system. We tested the hypothesis that intronic antisense long non-coding RNA, MGAT3-AS1, can predict delayed allograft function after kidney transplantation. We prospectively assessed kidney function and MGAT3-AS1 in 129 incident deceased donor kidney transplant recipients before and after transplantation. MGAT3-AS1 levels were measured in mononuclear cells using qRT-PCR. Delayed graft function was defined by at least one dialysis session within 7 days of transplantation. Delayed graft function occurred in 22 out of 129 transplant recipients (17%). Median MGAT3-AS1 after transplantation was significantly lower in patients with delayed graft function compared to patients with immediate graft function (6.5 × 10 −6 , IQR 3.0 × 10 −6 to 8.4 × 10 −6 ; vs. 8.3 × 10 −6 , IQR 5.0 × 10 −6 to 12.8 × 10 −6 ; p < 0.05). The median preoperative MGAT3-AS1 was significantly lower in kidney recipients with delayed graft function (5.1 × 10 −6 , IQR, 2.4 × 10 −6 to 6.8 × 10 −6 ) compared to recipients with immediate graft function (8.9 × 10 −6 , IQR, 6.8 × 10 −6 to 13.4 × 10 −6 ; p < 0.05). Receiver-operator characteristics showed that preoperative MGAT3-AS1 predicted delayed graft function (area under curve, 0.83; 95% CI, 0.65 to 1.00; p < 0.01). We observed a positive predictive value of 0.57, and a negative predictive value of 0.95. Long non-coding RNA, MGAT3-AS1, indicates short-term outcome in patients with deceased donor kidney transplantation.