NSD2 modulates Drp1-mediated mitochondrial fission in chronic renal allograft interstitial fibrosis by methylating STAT1.

Renal interstitial fibrosis/tubular atrophy (IF/TA) is a prominent pathological feature of chronic allograft dysfunction (CAD). Our previous study has demonstrated that epithelial-mesenchymal transition (EMT) plays a significant role in shaping the development of IF/TA. Nuclear SET domain (NSD2), a histone methyltransferase catalyzing methylation at lysine 36 of histone 3, is crucially involved in the development and progression of solid tumors. But its role in the development of renal allograft interstitial fibrosis has yet to be elucidated. Here, we characterize NSD2 as a crucial mediator in the mouse renal transplantation model in vivo and a model of tumor necrosis factor-α (TNF-α) stimulated-human renal tubular epithelial cells (HK-2) in vitro. Functionally, NSD2 knockdown inhibits EMT, dynamin-related protein 1 (Drp1)-mediated mitochondrial fission in mice. Conversely, NSD2 overexpression exacerbates fibrosis-associated phenotypes and mitochondrial fission in tubular cells. Mechanistically, tubular NSD2 aggravated the Drp-1 mediated mitochondrial fission via STAT1/ERK/PI3K/Akt signaling pathway in TNF-α-induced epithelial cell models. Momentously, mass spectrometry (MS) Analysis and site-directed mutagenesis assays revealed that NSD2 interacted with and induced Mono-methylation of STAT1 on K173, leading to its phosphorylation, IMB1-dependent nuclear translocation and subsequent influence on TNF-α-induced EMT and mitochondrial fission in NSD2-dependent manner. Collectively, these findings shed light on the mechanisms and suggest that targeting NSD2 could be a promising therapeutic approach to enhance tubular cell survival and alleviate interstitial fibrosis in renal allografts during CAD.

A retrospective study of mineral and bone disorder in kidney transplant recipients: Single-center experience.

BACKGROUND: The status of mineral and bone disorder (MBD) after kidney transplantation is not fully understood, and the assessment of abnormal mineral and bone metabolism in kidney transplant recipients (KTRs) has not been standardized. MATERIALS AND METHODS: We performed a retrospective analysis of 292 KTRs in our center. The levels of biochemical markers of bone metabolism and bone mineral density (BMD) were assessed. We evaluated the influencing factors of BMD using linear regression analysis. And correlation test was used for the correlation analysis between bone metabolism indicators and other indicators. RESULTS: Postoperative MBD mainly manifested as hypercalcemia (8.9%), hypophosphatemia (27.1%), low levels of 25-hydroxyvitamin D(25(OH)vitD) (67.0%), hyperparathyroidism (50.6%), and high levels of bone turnover markers (BTMs). The prevalence of osteopenia/osteoporosis in the femoral neck (FN) and lumbar spine (LS) was 20.1%/2.8% and 26.1%/3.6%, respectively. Multivariate analysis indicated that FN BMD was positively associated with body mass index (BMI) and negatively associated with acute rejection history (p < 0.05); while LS BMD was positively associated with BMI, and negatively associated with intact parathyroid hormone (iPTH) (p < 0.05). Biochemical markers of bone metabolism were affected by age, sex, preoperative dialysis mode and time, postoperative time, transplanted kidney function, and iPTH levels. LS BMD was negatively correlated with iPTH and BTMs (p < 0.05). CONCLUSION: MBD persisted after kidney transplantation. Decreased bone mass was associated with persistent hyperparathyroidism, acute rejection history, low BMI, advanced age, and menopause. Dynamic monitoring of bone metabolism index and BMD helps to assess MBD after kidney transplantation.

Macrophage polarization induces endothelium-to-myofibroblast transition in chronic allograft dysfunction.

Our research explores the role of M1 macrophage polarization in endothelium-to-myofibroblast transition (EndMT) and chronic allograft dysfunction (CAD). GSE21374 transcriptome sequencing data were obtained. Transplanted nephrectomy specimens from CAD patients were collected and studied to explore the infiltration of M1 and M2 macrophages using immunofluorescence, PCR, and Western blotting (WB). A co-culture model of M1 macrophages, polarized from mouse bone marrow-derived macrophages (BMDM) or Raw264.7, and aortic endothelial cells was established, and EndMT was tested using PCR and WB. RNA-sequencing was performed on the macrophages from the mouse BMDM. The TNF-α secreted from the polarized M1 macrophages was verified using ELISA. Based on the GEO public database, it was observed that macrophages were significantly infiltrated in CAD allograft tissues, with CD68(+) iNOS(+) M1 macrophages significantly infiltrating the glomeruli of allograft tissues, and CD68(+)CD206(+) M2 macrophages notably infiltrating the allograft interstitial area. The mRNA expression of the M1 macrophage marker inducible nitric oxide synthase (iNOS) was significantly increased (p < 0.05) and M1 macrophages were found to significantly promote the EndMT process in vitro. RNA-Sequencing analysis revealed that TNF signaling could be involved in the EndMT induced by M1 macrophages, and in vitro studies confirmed that TNF-α in the supernatant was significantly higher. The renal allograft tissues of CAD patients were found to be significantly infiltrated by M1 macrophages and could promote the progression of CAD by secreting the cytokine TNF-α to induce EndMT in endothelial cells.

Mineral and bone disorder after kidney transplantation: a single-center cohort study.

BACKGROUND: The assessment and prevention of mineral and bone disorder (MBD) in kidney transplant recipients (KTRs) have not been standardized. This study aimed to evaluate MBD one year after kidney transplantation (KT) and identify the influencing factors of MBD. METHODS: A total of 95 KTRs in our center were enrolled. The changes in bone mineral density (BMD) and bone metabolism biochemical markers, including serum calcium (Ca), phosphorus(P), 25-hydroxyvitamin D(25(OH)vitD), intact parathyroid hormone (iPTH), bone alkaline phosphatase, osteocalcin (OC), type I collagen N-terminal peptide and type I collagen C-terminal peptide (CTx), over one year after KT were assessed. The possible influencing factors of BMD were analyzed. The relationships between bone metabolism biochemical markers were evaluated. The indicators between groups with or without iPTH normalization were also compared. RESULTS: MBD after KT was manifested as an increased prevalence of hypophosphatemia and bone loss, persistent 25(OH)vitD deficiency, and partially decreased PTH and bone turnover markers (BTMs). Femoral neck BMD was positively correlated with body mass index (BMI) and postoperative 25(OH)vitD, and negatively correlated with postoperative PTH. Lumbar spine BMD was positively correlated with BMI and preoperative TG, and negatively correlated with preoperative OC and CTx. BMD loss was positively associated with glucocorticoid accumulation. Preoperative and postoperative iPTH was negatively correlated with postoperative serum P and 25(OH)vitD, and positively correlated with postoperative Ca and BTMs. The recipients without iPTH normalization, who accounted for 41.0% of all KTRs, presented with higher Ca, lower P, higher BTMs, advanced age, and a higher prevalence of preoperative parathyroid hyperplasia. CONCLUSIONS: MBD persisted after KT, showing a close relationship with hyperparathyroidism, high bone turnover, and glucocorticoid accumulation.

Solute carrier-related signature for assessing prognosis and immunity in patients with clear-cell renal cell carcinoma.

Background: Clear-cell renal cell carcinoma (ccRCC) is the most common malignant kidney cancer. However, the tumor microenvironment and crosstalk involved in metabolic reprogramming in ccRCC are not well-understood. Methods: We used The Cancer Genome Atlas to obtain ccRCC transcriptome data and clinical information. The E-MTAB-1980 cohort was used for external validation. The GENECARDS database contains the first 100 solute carrier (SLC)-related genes. The predictive value of SLC-related genes for ccRCC prognosis and treatment was assessed using univariate Cox regression analysis. An SLC-related predictive signature was developed through Lasso regression analysis and used to determine the risk profiles of patients with ccRCC. Patients in each cohort were separated into high- and low-risk groups based on their risk scores. The clinical importance of the signature was assessed through survival, immune microenvironment, drug sensitivity, and nomogram analyses using R software. Results: SLC25A23, SLC25A42, SLC5A1, SLC3A1, SLC25A37, SLC5A6, SLCO5A1, and SCP2 comprised the signatures of the eight SLC-related genes. Patients with ccRCC were separated into high- and low-risk groups based on the risk value in the training and validation cohorts; the high-risk group had a significantly worse prognosis (p < 0.001). The risk score was an independent predictive indicator of ccRCC in the two cohorts according to univariate and multivariate Cox regression (p < 0.05). Analysis of the immune microenvironment showed that immune cell infiltration and immune checkpoint gene expression differed between the two groups (p < 0.05). Drug sensitivity analysis showed that compared to the low-risk group, the high-risk group was more sensitive to sunitinib, nilotinib, JNK-inhibitor-VIII, dasatinib, bosutinib, and bortezomib (p < 0.001). Survival analysis and receiver operating characteristic curves were validated using the E-MTAB-1980 cohort. Conclusions: SLC-related genes have predictive relevance in ccRCC and play roles in the immunological milieu. Our results provide insight into metabolic reprogramming in ccRCC and identify promising treatment targets for ccRCC.

Association between fibrosis-related gene polymorphism and long-term allograft outcome in renal transplant recipients.

BACKGROUND: Renal allograft fibrosis is one of characteristic causes of long-term renal function loss. The purpose of our study is to investigate the association between fibrosis-related genes single nucleotide polymorphism (SNPs) and kidney function in 5 years after kidney transplantation. METHODS: A total of 143 recipients were eligible for screening with 5-year follow-up information and SNP sequencing information from blood samples were included in this study. Minor Allele Frequency (MAF) and Hardy-Weinberg Equilibrium (HWE) analysis was conducted to identify tagger single-nucleotide polymorphisms (SNPs) and haplotypes. SNPs associated with the fifth year chronic kidney disease (CKD) staging were screened by SPSS and the "SNPassoc" package in RStudio and used for subsequent prediction model construction. RESULTS: A total of 275 renal transplant-related SNPs identified after target sequencing analysis. 64 Tagger SNPs were selected, and two SNPs (rs13969 and rs243849) were statistically significant for stage of CKD in 5 years. Finally, a model based on Gender, Age, rs1396, and rs243849 was constructed by multivariate linear regression analysis. Additionally, this model has a good performance in predicting uremia five years after kidney transplantation. CONCLUSION: Two SNPs (rs13969 and rs243849) were identified to be significantly associated with long-term renal allograft function. Based on this, a prediction model for long-term allograft function was established containing Gender, Age, rs1396, and rs243849. However, an independent cohort should be enrolled to validate the predicting performance.

Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.

Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes. Methods: We screened all 162 "kidney transplant"-related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms-LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs-were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan-Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis. Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response-related pathways were found in the C4 group, which had poor prognosis. Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO.

Causal effects of human serum metabolites on occurrence and progress indicators of chronic kidney disease: a two-sample Mendelian randomization study.

Background: Chronic kidney disease (CKD) is often accompanied by alterations in the metabolic profile of the body, yet the causative role of these metabolic changes in the onset of CKD remains a subject of ongoing debate. This study investigates the causative links between metabolites and CKD by leveraging the results of genomewide association study (GWAS) from 486 blood metabolites, employing bulk two-sample Mendelian randomization (MR) analyses. Building on the metabolites that exhibit a causal relationship with CKD, we delve deeper using enrichment analysis to identify the metabolic pathways that may contribute to the development and progression of CKD. Methods: In conducting the Mendelian randomization analysis, we treated the GWAS data for 486 metabolic traits as exposure variables while using GWAS data for estimated glomerular filtration rate based on serum creatinine (eGFRcrea), microalbuminuria, and the urinary albumin-to-creatinine ratio (UACR) sourced from the CKDGen consortium as the outcome variables. Inverse-variance weighting (IVW) analysis was used to identify metabolites with a causal relationship to outcome. Using Bonferroni correction, metabolites with more robust causal relationships are screened. Additionally, the IVW-positive results were supplemented with the weighted median, MR-Egger, weighted mode, and simple mode. Furthermore, we performed sensitivity analyses using the Cochran Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out (LOO) test. Pathway enrichment analysis was conducted using two databases, KEGG and SMPDB, for eligible metabolites. Results: During the batch Mendelian randomization (MR) analyses, upon completion of the inverse-variance weighted (IVW) approach, sensitivity analysis, and directional consistency checks, 78 metabolites were found to meet the criteria. The following four metabolites satisfy Bonferroni correction: mannose, N-acetylornithine, glycine, and bilirubin (Z, Z), and mannose is causally related to all outcomes of CKD. By pathway enrichment analysis, we identified eight metabolic pathways that contribute to CKD occurrence and progression. Conclusion: Based on the present analysis, mannose met Bonferroni correction and had causal associations with CKD, eGFRcrea, microalbuminuria, and UACR. As a potential target for CKD diagnosis and treatment, mannose is believed to play an important role in the occurrence and development of CKD.

Biological Characteristics and Predictive Model of Biopsy-Proven Acute Rejection (BPAR) After Kidney Transplantation: Evidences of Multi-Omics Analysis.

Objectives: Early diagnosis and detection of acute rejection following kidney transplantation are of great significance for guiding the treatment and improving the prognosis of renal transplant recipients. In this study, we are aimed to explore the biological characteristics of biopsy-proven acute rejection (BPAR) and establish a predictive model. Methods: Gene expression matrix of the renal allograft samples in the GEO database were screened and included, using Limma R package to identify differentially expressed transcripts between BPAR and No-BPAR groups. Then a predictive model of BPAR was established based on logistic regression of which key transcripts involved in the predictive model were further explored using functional enrichment analyses including Gene Ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA). Results: A total of four studies (GSE129166, GSE48581, GSE36059, and GSE98320) were included for extensive analysis of differential expression. 32 differential expressed transcripts were observed to be significant between two groups after the pooled analysis. Afterward, a predictive model containing the five most significant transcripts (IDO1, CXCL10, IFNG, GBP1, PMAIP1) showed good predictive efficacy for BPAR after kidney transplantation (AUC = 0.919, 95%CI = 0.902-0.939). Results of functional enrichment analysis showed that The functions of differential genes are mainly manifested in chemokine receptor binding, chemokine activity, G protein-coupled receptor binding, etc. while the immune infiltration analysis indicated that immune cells mainly related to acute rejection include Macrophages. M1, T cells gamma delta, T cells CD4 memory activated, eosinophils, etc. Conclusion: We have identified a total of 32 differential expressed transcripts and based on that, a predictive model with five significant transcripts was established, which was suggested as a highly recommended tool for the prediction of BPAR after kidney transplantation. However, an extensive study should be performed for the evaluation of the predictive model and mechanism involved.