Renal ischemia-reperfusion injury (IRI) contributes to acute kidney injury (AKI), increases morbidity and mortality, and is a significant risk factor for chronic kidney disease (CKD). Macrophage infiltration is a common feature after renal IRI, and infiltrating macrophages can be polarized into the following two distinct types: M1 macrophages, i.e., classically activated macrophages, which can not only inhibit infection but also accelerate renal injury, and M2 macrophages, i.e., alternatively activated macrophages, which have a repair phenotype that can promote wound healing and subsequent fibrosis. The role of TSC1, which is a negative regulator of mTOR signaling that regulates macrophage polarization in inflammation-linked diseases, has been well documented, but whether TSC1 contributes to macrophage polarization in the process of IRI is still unknown. Here, by using a mouse model of renal ischemia-reperfusion, we found that myeloid cell-specific TSC1 knockout mice (termed Lyz-TSC1 cKO mice) had higher serum creatinine levels, more severe histological damage, and greater proinflammatory cytokine production than wild-type (WT) mice during the early phase after renal ischemia-reperfusion. Furthermore, the Lyz-TSC1 cKO mice showed attenuated renal fibrosis during the repair phase of IRI with decreased levels of M2 markers on macrophages in the operated kidneys, which was further confirmed in a cell model of hypoxia-reoxygenation (H/R) in vitro. Mechanistically, by using RNA sequencing of sorted renal macrophages, we found that the expression of most M1-related genes was upregulated in the Lyz-TSC1 cKO group (Supplemental Table 1) during the early phase. However, C/EBPβ and CD206 expression was decreased during the repair phase compared to in the WT group. Overall, our findings demonstrate that the expression of TSC1 in macrophages contributes to the whole process of IRI but serves as an inflammation suppressor during the early phase and a fibrosis promoter during the repair phase.

Renal ischemia-reperfusion injury (IRI) is an inevitable process in kidney transplantation, leading to acute kidney injury, delayed graft function (DGF), and even graft loss. Ferroptosis is an iron-dependent regulated cell death in various diseases including IRI. We aimed to identify subtypes of renal IRI and construct a robust DGF predictive signature based on ferroptosis-related genes (FRGs). A consensus clustering analysis was applied to identify ferroptosis-associated subtypes of 203 renal IRI samples in the GSE43974 dataset. The FRG-associated DGF predictive signature was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO), and its robustness was further verified in the validation set GSE37838. The present study revealed two ferroptosis-related patient clusters (pBECN1 and pNF2 cluster) in renal IRI samples based on distinct expression patterns of BECN1 and NF2 gene clusters. Cluster pBECN1 was metabolically active and closely correlated with less DGF, while pNF2 was regarded as the metabolic exhausted subtype with higher incidence of DGF. Additionally, a six-gene (ATF3, SLC2A3, CXCL2, DDIT3, and ZFP36) ferroptosis-associated signature was constructed to predict occurrence of DGF in renal IRI patients and exhibited robust efficacy in both the training and validation sets. High-risk patients tended to have more infiltration of dendritic cells, macrophages, and T cells, and they had significantly enriched chemokine-related pathway, WNT/β-catenin signaling pathway, and allograft rejection. Patients with low risks of DGF were associated with ferroptosis-related pathways such as glutathione and fatty acid metabolism pathways. In conclusion, patient stratification with distinct metabolic activities based on ferroptosis may help distinguish patients who may respond to metabolic therapeutics. Moreover, the DGF predictive signature based on FRGs may guide advanced strategies toward prevention of DGF in the early stage.

Objective. Renal ischemia–reperfusion (I/R) injury is a major cause of acute kidney injury (AKI) in transplanted kidneys. This study was aimed at exploring the role of PLK3 (polo-like kinase 3) in renal I/R injury, focusing on its relationship with oxidative stress-induced DNA damage and renal tubular epithelial cell (TEC) apoptosis. Methods. TRAP-seq data from the development dataset GSE52004 and the validation dataset GSE121191 were analyzed using GEO2R. PLK3 overexpression plasmids and targeted silencing siRNAs were used in a model of hypoxia/reoxygenation (H/R) injury, and rAAV-9-PLK3-KD were administered to C57BL/6J mice exposed to I/R injury. The ATM-specific inhibitor KU-60019 was used to block the DNA damage response (DDR). Western blotting was performed to measure DDR- and apoptosis-associated protein expression. Cell viability was measured by CCK-8 reagent, and apoptosis was examined by flow cytometry and TUNEL assay. Furthermore, the fluorescent probes H2DCFH-DA and DHE were used to measure ROS production in vitro. The MDA level and SOD activity were measured to assess oxidative stress in vivo. KIM-1 staining and Scr and BUN were used to evaluate kidney injury. Results. The mRNA and protein levels of PLK3 were markedly increased in the H/R injury and I/R injury models. GO terms showed that PLK3 was mainly involved in oxidative stress and DNA damage after renal I/R injury. Overexpression of PLK3 decreased cell viability and increased apoptosis. In contrast, targeted silencing of PLK3 expression decreased the Bax/Bcl-2 ratio by decreasing P53 phosphorylation, thereby reducing TEC apoptosis. Furthermore, KU-60019 reduced PLK3 activation and DDR-induced apoptosis, while overexpression of PLK3 reversed the mitigating effect of KU-60019 on TEC apoptosis. Similarly, rAAV-9-PLK3 KD mice exhibited a lower rate of TEC apoptosis and milder renal damage after I/R injury. Conclusion. We demonstrate for the first time that PLK3 is involved in oxidative stress-induced DNA damage and TEC apoptosis in renal I/R injury. Inhibition of PLK3 attenuates TEC apoptosis after I/R injury by blocking the ATM/P53-mediated DDR. Therefore, PLK3 may serve as a potential therapeutic target for ischemic AKI.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Redox metabolism has been recognized as the hallmark of cancer. But the concrete role of redox-related genes in patient stratification of ccRCC remains unknown. Herein, we aimed to characterize the molecular features of ccRCC based on the redox gene expression profiles from The Cancer Genome Atlas. Differentially expressed redox genes (DERGs) and vital genes in metabolism regulation were identified and analyzed in the ccRCC. Consensus clustering was performed to divide patients into three clusters (C1, C2, and C3) based on 139 redox genes with median FPKM   value > 1 . We analyzed the correlation of clusters with clinicopathological characteristics, immune infiltration, gene mutation, and response to immunotherapy. Subclass C1 was metabolic active with moderate prognosis and associated with glucose, lipid, and protein metabolism. C2 had intermediate metabolic activity with worse prognosis and correlated with more tumor mutation burden, neoantigen, and aneuploidy, indicating possible drug sensitivities towards immune checkpoint inhibitors. Metabolic exhausted subtype C3 showed high cytolytic activity score, suggesting better prognosis than C1 and C2. Moreover, the qRT-PCR was performed to verify the expression of downregulated DERGs including ALDH6A1, ALDH1L1, GLRX5, ALDH1A3, and GSTM3, and upregulated SHMT1 in ccRCC. Overall, our study provides an insight into the characteristics of molecular classification of ccRCC patients based on redox genes, thereby deepening the understanding of heterogeneity of ccRCC and allowing prediction of prognosis of ccRCC patients.

Background Ischemia-reperfusion injury and drug-induced nephrotoxicity are the two most common reasons for acute kidney injury (AKI). However, little attention has been paid to early activation of fibroblasts in the progression of AKI to chronic kidney disease (CKD). The present study aimed to identify related genes and pathways on fibroblast activation in two mouse models of AKI: ischemia-reperfusion injury (IRI) model and folic acid (FA)-induced injury model. Methods The microarray expression profiles of GSE62732 and GSE121190 were downloaded from the GEO database, and the differentially expressed genes (DEGs) was analyzed using the Limma package of R software. Principal component analysis (PCA) was also performed using R. The functional information of gene products was annotated by Gene Ontology (GO) and DAVID online database, and the pathway analysis was carried out by using the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) database. Protein-protein interactions (PPI) network was constructed by STRING and Cytoscape. Furthermore, in the Hypoxia/Reoxygenation (H/R) model, the morphological changes of cells were observed under microscope and the expression of the hub genes in NRK-49F cells were validated by qRT-PCR assays. Results A total of 457 DEGs were identified. Among these, 215 DEGs were upregulated and 242 DEGs were downregulated in the acute injured samples compared with uninjured samples. The GO enrichment analysis indicated that these DEGs were mainly involved in transport, the oxidation-reduction process, the metabolic process, metal ion binding, hydrolase activity, and oxidoreductase activity. The KEGG analysis revealed that these DEGs were significantly enriched in the PI3K-Akt signaling pathway, protein digestion and absorption pathway, and focal adhesion pathway. The hub genes including Hnf4α, Pck1 and Timp1 were validated by the qRT-PCR assay in NRK-49F cells in the H/R model. Conclusions Hnf4α, Pck1 and Timp-1 may play a pivotal role in the early activation of fibroblasts, providing novel therapeutic strategies for early prediction and treatment of renal fibrosis.

BackgroundPosttransplant renal function is critically important for kidney transplant recipients. Accurate prediction of graft function would greatly help in deciding acceptance or discard of allocated kidneys.Methods: Whole-slide images (WSIs) of H&E-stained donor kidney biopsies at × 200 magnification between January 2015 and December 2019 were collected. The clinical characteristics of each donor and corresponding recipient were retrieved. Graft function was indexed with a stable estimated glomerular filtration rate (eGFR) and reduced graft function (RGF). We used convolutional neural network (CNN)-based models, such as EfficientNet-B5, Inception-V3, and VGG19 for the prediction of these two outcomes.ResultsIn total, 219 recipients with H&E-stained slides of the donor kidneys were included for analysis [biopsies from standard criteria donor (SCD)/expanded criteria donor (ECD) was 191/28]. The results showed distinct improvements in the prediction performance of the deep learning algorithm plus the clinical characteristics model. The EfficientNet-B5 plus clinical data model showed the lowest mean absolute error (MAE) and root mean square error (RMSE). Compared with the clinical data model, the area under the receiver operating characteristic (ROC) curve (AUC) of the clinical data plus image model for eGFR classification increased from 0.69 to 0.83. In addition, the predictive performance for RGF increased from 0.66 to 0.80. Gradient-weighted class activation mappings (Grad-CAMs) showed that the models localized the areas of the tubules and interstitium near the glomeruli, which were discriminative features for RGF.ConclusionOur results preliminarily show that deep learning for formalin-fixed paraffin-embedded H&E-stained WSIs improves graft function prediction accuracy for deceased-donor kidney transplant recipients.

Objectives: We aimed to analyze the effect of cold ischemia time (CIT) on post-transplant graft function through mixed-effect model analysis to reduce the bias caused by paired mate kidneys.Methods: We reviewed all kidney transplantation records from 2015 to 2019 at our center. After applying the exclusion criteria, 561 cases were included for analysis. All donor characteristics, preservation and matching information, and recipient characteristics were collected. Transplant outcomes included delayed graft function (DGF) and estimated glomerular filtration rate (eGFR). Generalized linear mixed models were applied for analysis. We also explored potential effect modifiers, namely, donor death category, expanded criteria donors, and donor death causes.Results: Among the 561 cases, 79 DGF recipients developed DGF, and 15 recipients who died after surgery were excluded from the eGFR estimation. The median stable eGFR of the 546 recipients was 60.39 (47.63, 76.97) ml/min/1.73 m2. After adjusting for confounding covariates, CIT had a negative impact on DGF incidence [odds ratio = 1.149 (1.006, 1.313), P = 0.041]. In the evaluation of the impact on eGFR, the regression showed that CIT had no significant correlation with eGFR [β = −0.287 (−0.625, 0.051), P = 0.096]. When exploring potential effect modifiers, only the death category showed a significant interaction with CIT in the effect on eGFR (Pinteraction = 0.027). In the donation after brain death (DBD) group, CIT had no significant effect on eGFR [β = 0.135 (−0.433, 0.702), P = 0.642]. In the donation after circulatory death/donation after brain death followed by circulatory death (DCD/DBCD) group, CIT had a significantly negative effect on eGFR [β= −0.700 (−1.196, −0.204), P = 0.006]. Compared to a CIT of 0–6 h, a CIT of 6–8 or 8–12 h did not decrease the post-transplant eGFR. CIT over 12 h (12–16 h or over 16 h) significantly decreased eGFR. With the increase in CIT, the regenerated eGFR worsened (Ptrend = 0.011).Conclusion: Considering the effect of paired mate kidneys, the risk of DGF increased with prolonged CIT. The donor death category was an effect modifier between CIT and eGFR. Prolonged CIT did not reduce the eGFR level in recipients from DBDs but significantly decreased the eGFR in recipients from DCDs/DBCDs. This result indicates the potential biological interaction between CIT and donor death category.

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