Single-Cell Transcriptome Analysis of Chronic Antibody-Mediated Rejection After Renal Transplantation

Oxidized ATP Suppresses B Lymphocyte Activity to Attenuate Antibody-mediated Rejection of Kidney Allografts in Mice

BACKGROUND

Antibody-mediated rejection (AMR) is a major cause of renal allograft dysfunction and loss. Targeting B cells and/or donor-specific antibody removal using plasma exchange and anti-CD20 antibodies are increasingly used in clinical practice, but the efficacy remains limited. Recent studies suggest that targeting purinergic P2X7 receptor/ATP axis can have profound immune regulatory effects in transplant models, but the mechanisms involved remain incompletely defined.

METHODS

Purified B cells were isolated from the spleen of Balb/C mice and cultured with oxidized ATP at different concentrations. Proliferation and differentiation of B cells were examined. Effects of oxidized ATP were examined in a presensitized animal model where kidney allograft rejection mimics aspects of clinical AMR. Histopathology was assessed at the time of rejection or on day 5 after kidney transplantation. Infiltrating immune cells in renal allografts were detected by flow cytometry.

RESULTS

Oxidized ATP inhibited B-cell activation and proliferation in vitro, significantly attenuated histological signs of graft injury and prolonged kidney allograft survival. Mechanistically, oxidized ATP inhibited antibody secretion by activated B cells in response to lipopolysaccharide stimulation and markedly suppressed the production of donor-specific antibody in kidney allograft recipients. Oxidized ATP also reduced graft infiltration by other inflammatory cells.

CONCLUSIONS

These findings provide evidence for the involvement of the purinergic P2X7 receptor pathway in AMR and suggest that targeting this pathways may have important clinical implications.

Neutrophil depletion attenuates antibody-mediated rejection in a renal transplantation mouse model

Antibody-mediated rejection (AMR) can cause graft failure following renal transplantation. Neutrophils play a key role in AMR progression, but the exact mechanism remains unclear. We investigated the effect of neutrophils on AMR in a mouse kidney transplantation model. The mice were divided into five groups: syngeneic transplantation (Syn), allograft transplantation (Allo), and three differently treated AMR groups. The AMR mouse model was established using skin grafts to pre-sensitize recipient mice. Based on the AMR model, Ly6G-specific monoclonal antibodies were administered to deplete neutrophils (NEUT-/- + AMR) and TACI-Fc was used to block B-cell-activating factor (BAFF)/a proliferation-inducing ligand (APRIL) signaling (TACI-Fc + AMR). Pathological changes were assessed using hematoxylin-eosin and immunohistochemical staining. Banff values were evaluated using the Banff 2015 criteria. Donor-specific antibody (DSA) levels were assessed using flow cytometry, and BAFF and APRIL concentrations were measured using ELISA. Compared to the Syn and Allo groups, a significantly increased number of neutrophils and increased C4d and IgG deposition were observed in AMR mice, accompanied by elevated DSA levels. Neutrophil depletion inhibited inflammatory cell infiltration and reduced C4d and IgG deposition. Neutrophil depletion significantly decreased DSA levels after transplantation and suppressed BAFF and APRIL concentrations, suggesting a mechanism for attenuating AMR-induced graft damage. Similar results were obtained after blockading BAFF/APRIL using a TACI-Fc fusion protein. In summary, neutrophil infiltration increased in the AMR mouse renal transplantation model. Neutrophil depletion or blockading the BAFF/APRIL signaling pathway significantly alleviated AMR and may provide better options for the clinical treatment of AMR.

Prediction of Acute Cardiac Rejection Based on Gene Expression Profiles

Acute cardiac rejection remains a significant challenge in the post-transplant period, necessitating meticulous monitoring and timely intervention to prevent graft failure. Thus, the goal of the present study was to identify novel biomarkers involved in acute cardiac rejection, paving the way for personalized diagnostic, preventive, and treatment strategies. A total of 809 differentially expressed genes were identified in the GSE150059 dataset. We intersected genes selected by analysis of variance, recursive feature elimination, least absolute shrinkage and selection operator, and random forest classifier to identify the most relevant genes involved in acute cardiac rejection. Thus, HCP5, KLRD1, GZMB, PLA1A, GNLY, and KLRB1 were used to train eight machine learning models: random forest, logistic regression, decision trees, support vector machines, gradient boosting machines, K-nearest neighbors, XGBoost, and neural networks. Models were trained, tested, and validated on the GSE150059 dataset (MMDx-based diagnosis of rejection). Eight algorithms achieved great performance in predicting acute cardiac rejection. However, all machine learning models demonstrated poor performance in two external validation sets that had rejection diagnosis based on histology: merged GSE2596 and GSE4470 dataset and GSE9377 dataset, thus highlighting differences between these two methods. According to SHAP and LIME, KLRD1 and HCP5 were the most impactful genes.

Single-Cell RNA Sequencing in Organ and Cell Transplantation

Single-cell RNA sequencing is a high-throughput novel method that provides transcriptional profiling of individual cells within biological samples. This method typically uses microfluidics systems to uncover the complex intercellular communication networks and biological pathways buried within highly heterogeneous cell populations in tissues. One important application of this technology sits in the fields of organ and stem cell transplantation, where complications such as graft rejection and other post-transplantation life-threatening issues may occur. In this review, we first focus on research in which single-cell RNA sequencing is used to study the transcriptional profile of transplanted tissues. This technology enables the analysis of the donor and recipient cells and identifies cell types and states associated with transplant complications and pathologies. We also review the use of single-cell RNA sequencing in stem cell implantation. This method enables studying the heterogeneity of normal and pathological stem cells and the heterogeneity in cell populations. With their remarkably rapid pace, the single-cell RNA sequencing methodologies will potentially result in breakthroughs in clinical transplantation in the coming years.

Interferon-γ and its response are determinants of antibody-mediated rejection and clinical outcomes in patients after renal transplantation

Interferon-γ (IFN-γ) is an important cytokine in tissue homeostasis and immune response, while studies about it in antibody-mediated rejection (ABMR) are very limited. This study aims to comprehensively elucidate the role of IFN-γ in ABMR after renal transplantation. In six renal transplantation cohorts, the IFN-γ responses (IFNGR) biological process was consistently top up-regulated in ABMR compared to stable renal function or even T cell-mediated rejection in both allografts and peripheral blood. According to single-cell analysis, IFNGR levels were found to be broadly elevated in most cell types in allografts and peripheral blood with ABMR. In allografts with ABMR, M1 macrophages had the highest IFNGR levels and were heavily infiltrated, while kidney resident M2 macrophages were nearly absent. In peripheral blood, CD14+ monocytes had the top IFNGR level and were significantly increased in ABMR. Immunofluorescence assay showed that levels of IFN-γ and M1 macrophages were sharply elevated in allografts with ABMR than non-rejection. Importantly, the IFNGR level in allografts was identified as a strong risk factor for long-term renal graft survival. Together, this study systematically analyzed multi-omics from thirteen independent cohorts and identified IFN-γ and IFNGR as determinants of ABMR and clinical outcomes in patients after renal transplantation.

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

Identification of antigen-presentation related B cells as a key player in Crohn's disease using single-cell dissecting, hdWGCNA, and deep learning

Crohn's disease (CD) arises from intricate intercellular interactions within the intestinal lamina propria. Our objective was to use single-cell RNA sequencing to investigate CD pathogenesis and explore its clinical significance. We identified a distinct subset of B cells, highly infiltrated in the CD lamina propria, that expressed genes related to antigen presentation. Using high-dimensional weighted gene co-expression network analysis and nine machine learning techniques, we demonstrated that the antigen-presenting CD-specific B cell signature effectively differentiated diseased mucosa from normal mucosa (Independent external testing AUC = 0.963). Additionally, using MCPcounter and non-negative matrix factorization, we established a relationship between the antigen-presenting CD-specific B cell signature and immune cell infiltration and patient heterogeneity. Finally, we developed a gene-immune convolutional neural network deep learning model that accurately diagnosed CD mucosa in diverse cohorts (Independent external testing AUC = 0.963). Our research has revealed a population of B cells with a potential promoting role in CD pathogenesis and represents a fundamental step in the development of future clinical diagnostic tools for the disease.

Mechanism underlying the effect of Pulsatilla decoction in hepatocellular carcinoma treatment: a network pharmacology and in vitro analysis

BACKGROUND

Currently, hepatocellular carcinoma (HCC) is associated with a poor prognosis. Moreover, there exist limited strategies for treating HCC. Pulsatilla decoction (PD), a traditional Chinese medicine formula, has been used to treat inflammatory bowel disease and several cancer types. Accordingly, we explored the mechanism of PD in HCC treatment via network pharmacology and in vitro experiments.

METHODS

Online databases were searched for gene data, active components, and potential target genes associated with HCC development. Subsequently, bioinformatics analysis was performed using protein-protein interaction and Network Construction and Kyoto Encyclopedia of Genes and Genomes (KEGG) to screen for potential anticancer components and therapeutic targets of PD. Finally, the effect of PD on HCC was further verified by in vitro experiments.

RESULTS

Network pharmacological analysis revealed that 65 compounds and 180 possible target genes were associated with the effect of PD on HCC. These included PI3K, AKT, NF-κB, FOS, and NFKBIA. KEGG analysis demonstrated that PD exerted its effect on HCC mainly via the PI3K-AKT, IL-17, and TNF signaling pathways. Cell viability and cell cycle experiments revealed that PD could significantly inhibit cancer cell proliferation and kill HCC cells by inducing apoptosis. Furthermore, western blotting confirmed that apoptosis was mediated primarily via the PI3K-AKT, IL-17, and TNF signaling pathways.

CONCLUSION

To the best of our knowledge, this is the first study to elucidate the molecular mechanism and potential targets of PD in the treatment of HCC using network pharmacology.

Identification and validation of ubiquitination-related signature and subgroups in immune microenvironment of tuberculosis

BACKGROUND

Mycobacterium tuberculosis (Mtb) is the bacterial pathogen responsible for causing tuberculosis (TB), a severe public health concern that results in numerous deaths worldwide. Ubiquitination (Ub) is an essential physiological process that aids in maintaining homeostasis and contributes to the development of TB. Therefore, the main objective of our study was to investigate the potential role of Ub-related genes in TB.

METHODS

Our research entailed utilizing single sample gene set enrichment analysis (ssGSEA) in combination with several machine learning techniques to discern the Ub-related signature of TB and identify potential diagnostic markers that distinguish TB from healthy controls (HC).

RESULTS

In summary, we used the ssGSEA algorithm to determine the score of Ub families (E1, E2, E3, DUB, UBD, and ULD). Notably, the score of E1, E3, and UBD were lower in TB patients than in HC individuals, and we identified 96 Ub-related differentially expressed genes (UbDEGs). Employing machine learning algorithms, we identified 11 Ub-related hub genes and defined two distinct Ub-related subclusters. Notably, through GSVA and functional analysis, it was determined that these subclusters were implicated in numerous immune-related processes. We further investigated these Ub-related hub genes in four TB-related diseases and found that TRIM68 exhibited higher correlations with various immune cells in different conditions, indicating that it may play a crucial role in the immune process of these diseases.

CONCLUSION

The observed enrichment of Ub-related gene expression in TB patients emphasizes the potential involvement of ubiquitination in the progression of TB. These significant findings establish a basis for future investigations to elucidate the molecular mechanisms associated with TB, select suitable diagnostic biomarkers, and design innovative therapeutic interventions for combating this fatal infectious disease.

Comprehensive analysis of necroptosis-related genes in renal ischemia-reperfusion injury

Background

Oxidative stress is the primary cause of ischemia-reperfusion injury (IRI) in kidney transplantation, leading to delayed graft function (DGF) and implications on patient health. Necroptosis is believed to play a role in renal IRI. This research presents a comprehensive analysis of necroptosis-related genes and their functional implications in the context of IRI in renal transplantation.

Methods

The necroptosis-related differentially expressed genes (NR-DEGs) were identified using gene expression data from pre- and post-reperfusion renal biopsies, and consensus clustering analysis was performed to distinguish necroptosis-related clusters. A predictive model for DGF was developed based on the NR-DEGs and patients were divided into high- and low-risk groups. We investigated the differences in functional enrichment and immune infiltration between different clusters and risk groups and further validated them in single-cell RNA-sequencing (scRNA-seq) data. Finally, we verified the expression changes of NR-DEGs in an IRI mouse model.

Results

Five NR-DEGs were identified and were involved in various biological processes. The renal samples were further stratified into two necroptosis-related clusters (C1 and C2) showing different occurrences of DGF. The predictive model had a reliable performance in identifying patients at higher risk of DGF with the area under the curve as 0.798. Additionally, immune infiltration analysis indicated more abundant proinflammatory cells in the high-risk group, which was also found in C2 cluster with more DGF patients. Validation of NR-DEG in scRNA-seq data further supported their involvement in immune cells. Lastly, the mouse model validated the up-regulation of NR-DEGs after IR and indicated the correlations with kidney function markers.

Conclusions

Our research provides valuable insights into the identification and functional characterization of NR-DEGs in the context of renal transplantation and sheds light on their involvement in immune responses and the progression of IRI and DGF.

Serum CXCL8 Concentration Can Be Used as a Noninvasive Marker of Subclinical Rejection After Pediatric Liver Transplantation

BACKGROUND

This study aimed to explore whether serum CXCL8 concentration can be used as a noninvasive marker of subclinical rejection (SCR) after pediatric liver transplantation (pLT).

METHODS

Firstly, RNA sequencing (RNA-seq) was performed on 22 protocol liver biopsy samples. Secondly, several experimental methods were used to verify the RNA-seq results. Finally, the clinical data and serum samples of 520 LT patients in the Department of Pediatric Transplantation of Tianjin First Central Hospital from January 2018 to December 2019 were collected.

RESULTS

RNA-seq results indicated that CXCL8 was significantly increased in the SCR group. The results of the 3 experimental methods were consistent with RNA-seq results. According to the 1:2 propensity score matching, 138 patients were divided into the SCR (n = 46) and non-SCR (n = 92) groups. Serological test results indicated that there was no difference in preoperative CXCL8 concentration between the SCR and non-SCR groups ( P  > 0.05). However, during protocol biopsy, CXCL8 in the SCR group was significantly higher than in the non-SCR group ( P  < 0.001). In diagnosing SCR, receiver operating characteristic curve analysis showed that the area under the curve of CXCL8 was 0.966 (95% confidence interval, 0.938-0.995), sensitivity was 95%, and specificity was 94.6%. In differentiating nonborderline from borderline rejection, the area under the curve of CXCL8 was 0.853 (95% confidence interval, 0.718-0.988), sensitivity was 86.7%, and specificity was 94.6%.

CONCLUSIONS

This study demonstrates that serum CXCL8 concentration has high accuracy for the diagnosis and disease stratification of SCR after pLT.

Vitamin D supplementation reduced blood inflammatory cytokines expression and improved graft function in kidney transplant recipients

Background

Chronic allograft dysfunction(CAD) is the leading cause of graft loss in kidney transplant recipients (KTRs). Inflammatory process is believed to be one of the major contributors to CAD. The aim of this study is to explore the anti-inflammatory effect of vitamin D (VD) supplementation in KTRs and its role in the graft function improvement(protection).

Methods

A retrospective cohort of 39 KTRs with chronic antibody mediated rejection(CAMR)or stable renal function and a prospective cohort of 42 KTRs treated or untreated with VD were enrolled. Serum levels of vitamin D metabolism and serum inflammatory cytokines, renal graft function, and routine blood biomarkers were tested and dynamically tracked within 12 months post-transplant.

Results

Compared with the stable group, the CAMR group exhibited significantly elevated serum levels of inflammatory cytokines IL-1β, IFN-γ, IL-2, IL-10, IP-10, and HMGB1 (P <0.05). The supplementation of vitamin D effectively increased the serum concentration of vitamin D in kidney transplant recipients (KTRs) in the treated group. During the course of treatment, the treated group exhibited a gradual increase in eGFR levels, which were significantly higher than those observed in the untreated group at 12 months post-transplant (p<0.05). Notably, as eGFR improved, there was a significant decrease in levels of IL-1β, IFN-γ, IL-2, IL-10, IP-10 and HMGB1 in the treated group compared to the untreated group (P<0.05).

Conclusion

This study confirmed that immune-inflammation is a crucial factor in the development of CAD in KTRs.VD deficiency impairs its anti-inflammatory activity. By assisting in the regulation of excessive immune inflammation and restoration of immune homeostasis, effective VD supplementation contributes to protection and maintenance of graft function in KTRs.

The central inflammatory regulator IκBζ: induction, regulation and physiological functions

IκBζ (encoded by NFKBIZ) is the most recently identified IkappaB family protein. As an atypical member of the IkappaB protein family, NFKBIZ has been the focus of recent studies because of its role in inflammation. Specifically, it is a key gene in the regulation of a variety of inflammatory factors in the NF-KB pathway, thereby affecting the progression of related diseases. In recent years, investigations into NFKBIZ have led to greater understanding of this gene. In this review, we summarize the induction of NFKBIZ and then elucidate its transcription, translation, molecular mechanism and physiological function. Finally, the roles played by NFKBIZ in psoriasis, cancer, kidney injury, autoimmune diseases and other diseases are described. NFKBIZ functions are universal and bidirectional, and therefore, this gene may exert a great influence on the regulation of inflammation and inflammation-related diseases.

Porcine anti-human lymphocyte immunoglobulin depletes the lymphocyte population to promote successful kidney transplantation

Introduction

Porcine anti-human lymphocyte immunoglobulin (pALG) has been used in kidney transplantation, but its impacts on the lymphocyte cell pool remain unclear.

Methods

We retrospectively analyzed 12 kidney transplant recipients receiving pALG, and additional recipients receiving rabbit anti-human thymocyte immunoglobulin (rATG), basiliximab, or no induction therapy as a comparison group.

Results

pALG showed high binding affinity to peripheral blood mononuclear cells (PBMCs) after administration, immediately depleting blood lymphocytes; an effect that was weaker than rATG but stronger than basiliximab. Single-cell sequencing analysis showed that pALG mainly influenced T cells and innate immune cells (mononuclear phagocytes and neutrophils). By analyzing immune cell subsets, we found that pALG moderately depleted CD4+T cells, CD8+T cells, regulatory T cells, and NKT cells and mildly inhibited dendritic cells. Serum inflammatory cytokines (IL-2, IL-6) were only moderately increased compared with rATG, which might be beneficial in terms of reducing the risk of untoward immune activation. During 3 months of follow-up, we found that all recipients and transplanted kidneys survived and showed good organ function recovery; there were no cases of rejection and a low rate of complications.

Discussion

In conclusion, pALG acts mainly by moderately depleting T cells and is thus a good candidate for induction therapy for kidney transplant recipients. The immunological features of pALG should be exploited for the development of individually-optimized induction therapies based on the needs of the transplant and the immune status of the patient, which is appropriate for non-high-risk recipients.

"Transplantomics" for predicting allograft rejection: real-life applications and new strategies from Network Medicine

Although decades of the reductionist approach achieved great milestones in optimizing the immunosuppression therapy, traditional clinical parameters still fail in predicting both acute and chronic (mainly) rejection events leading to higher rates across all solid organ transplants. To clarify the underlying immune-related cellular and molecular mechanisms, current biomedical research is increasingly focusing on "transplantomics" which relies on a huge quantity of big data deriving from genomics, transcriptomics, epigenomics, proteomics, and metabolomics platforms. The AlloMap (gene expression) and the AlloSure (donor-derived cell-free DNA) tests represent two successful examples of how omics and liquid biopsy can really improve the precision medicine of heart and kidney transplantation. One of the major challenges in translating big data in clinically useful biomarkers is the integration and interpretation of the different layers of omics datasets. Network Medicine offers advanced bioinformatic-molecular strategies which were widely used to integrate large omics datasets and clinical information in end-stage patients to prioritize potential biomarkers and drug targets. The application of network-oriented approaches to clarify the complex nature of graft rejection is still in its infancy. Here, we briefly discuss the real-life clinical applications derived from omics datasets as well as novel opportunities for establishing predictive tests in solid organ transplantation. Also, we provide an original "graft rejection interactome" and propose network-oriented strategies which can be useful to improve precision medicine of solid organ transplantation.

Prognostic and immune microenvironment analysis of cuproptosis-related LncRNAs in breast cancer

Breast cancer is the most common tumor and the leading cause of cancer death in women. Cuproptosis is a new type of cell death, which can induce proteotoxic stress and eventually lead to cell death. Therefore, regulating copper metabolism in tumor cells is a new therapeutic approach. Long non-coding RNAs play an important regulatory role in immune response. At present, cuproptosis-related lncRNAs in breast cancer have not been reported. Breast cancer RNA sequencing, genomic mutations, and clinical data were downloaded from The Cancer Genome Atlas (TCGA). Patients with breast cancer were randomly assigned to the train group or the test group. Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-related lncRNAs and to construct a risk prognostic model. The prediction performance of the model is verified and recognized. In addition, the nomogram was used to predict the prognosis of breast cancer patients. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassay were used to detect the differences in biological function. Tumor mutation burden (TMB) was used to measure immunotherapy response. A total of 19 cuproptosis genes were obtained and a prognostic model based on 10 cuproptosis-related lncRNAs was constructed. Kaplan-Meier survival curves showed statistically significant overall survival (OS) between the high-risk and low-risk groups. Receiver operating characteristic curve (ROC) and principal component analysis (PCA) show that the model has accurate prediction ability. Compared with other clinical features, cuproptosis-related lncRNAs model has higher diagnostic efficiency. Univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic factor for breast cancer patients. In addition, the nomogram model analysis showed that the tumor mutation burden was significantly different between the high-risk and low-risk groups. Of note, the additive effect of patients in the high-risk group and patients with high TMB resulted in reduced survival in breast cancer patients. Our study identified 10 cuproptosis-related lncRNAs, which may be promising biomarkers for predicting the survival prognosis of breast cancer.

Applications of Transcriptomics in the Research of Antibody-Mediated Rejection in Kidney Transplantation: Progress and Perspectives

Antibody-mediated rejection (ABMR) is the major cause of chronic allograft dysfunction and loss in kidney transplantation. The immunological mechanisms of ABMR that have been featured in the latest studies indicate a highly complex interplay between various immune and nonimmune cell types. Clinical diagnostic standards have long been criticized for being arbitrary and the lack of accuracy. Transcriptomic approaches, including microarray and RNA sequencing of allograft biopsies, enable the identification of differential gene expression and the continuous improvement of diagnostics. Given that conventional bulk transcriptomic approaches only reflect the average gene expression but not the status at the single-cell level, thereby ignoring the heterogeneity of the transcriptome across individual cells, single-cell RNA sequencing is rising as a powerful tool to provide a high-resolution transcriptome map of immune cells, which allows the elucidation of the pathogenesis and may facilitate the development of novel strategies for clinical treatment of ABMR.

Identification of renal ischemia reperfusion injury subtypes and predictive strategies for delayed graft function and graft survival based on neutrophil extracellular trap-related genes

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Caspase-8 activation in neutrophils facilitates autoimmune kidney vasculitis through regulating CD4+ effector memory T cells

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAVs) are closely associated with neutrophil recruitment and activation, but the impact of the neutrophil apoptosis process in autoimmune disease has been rarely explained. Here, by integrating and analyzing single-cell transcriptome datasets, we found that the caspase-8-associated pathway in neutrophils was highly activated in the kidney rather than in the blood. To verify the function of caspase-8 in neutrophils on AAVs progression, we constructed neutrophil-specific caspase-8 knockout mice combined with an AAVs model induced by human ANCA from AAVs patients, a rapid and powerful model developed in this study. Our results show that caspase-8 activation of neutrophils up-regulates the expression of several inflammatory and immunoregulatory factors, especially IL23A, regulating the activation and differentiation of tissue-resident CD4⁺ effector memory T cells. This study reveals that the activation of caspase-8 in neutrophils can worsen glomerulonephritis of AAVs by regulating inflammation and immunity.