Objective To identify immune-related transcription factors (TFs) in renal glomeruli and tubules from diabetic kidney disease (DKD) patients by bioinformatics analysis. Methods Gene expression datasets from GEO (GSE30528, GSE30529) and RNA sequencing (RNA-seq) data from the Karolinska Kidney Research Center were used. Gene set enrichment analysis (GSEA) was conducted to examine differences in immune-related gene expression in the glomeruli and tubules (DKD) patients. To identify immune-related genes (IRGs) and TFs, differential expression analysis was carried out using the Limma and DESeq2 software packages. Key immune-related TFs were pinpointed through co-expression analysis. The interaction network between TFs and IRGs was constructed using the STRING database and Cytoscape software. Furthermore, the Nephroseq database was employed to investigate the correlation between the identified TFs and clinical-pathological features. Results When compared to normal control tissues, significant differences in the expression of immune genes were observed in both the glomeruli and tubules of individuals with Diabetic Kidney Disease (DKD). Through differential and co-expression analysis, 50 immune genes and 9 immune-related transcription factors (TFs) were identified in the glomeruli. In contrast, 131 immune response genes (IRGs) and 41 immune-related TFs were discovered in the renal tubules. The protein-protein interaction (PPI) network highlighted four key immune-related TFs for the glomeruli: Interferon regulatory factor 8 (IRF8), lactotransferrin (LTF), CCAAT/enhancer binding protein alpha (CEBPA), and Runt-related transcription factor 3 (RUNX3). For the renal tubules, the key immune-related TFs were FBJ murine osteosarcoma viral oncogene homolog B (FOSB), nuclear receptor subfamily 4 group A member 1 (NR4A1), IRF8, and signal transducer and activator of transcription 1 (STAT1). These identified TFs demonstrated a significant correlation with the glomerular filtration rate (GFR), highlighting their potential importance in the pathology of DKD. Conclusion Bioinformatics analysis identifies potential genes associated with DKD pathogenesis and immune dysregulation. Further validation of the expression and function of these genes may contribute to immune-based therapeutic research for DKD.

IgA nephropathy (IgAN), which has been confirmed as a complement mediated autoimmune disease, is also one form of glomerulonephritis associated with COVID-19. Here, we aim to investigate the clinical and immunological characteristics of patients with IgAN after COVID-19. The level of plasma level of C5a (p < 0.001), soluble C5b-9 (p = 0.018), FHR5 (p < 0.001) were all significantly higher in Group CoV (33 patients with renal biopsy-proven IgAN experienced COVID-19) compared with Group non-CoV (44 patients with IgAN without COVID-19), respectively. Compared with Group non-CoV, the intensity of glomerular C4d (p = 0.017) and MAC deposition (p < 0.001) and Gd-IgA1 deposition (p = 0.005) were much stronger in Group CoV. Our finding revealed that for IgAN after COVID-19, mucosal immune responses to SARS-CoV-2 infection may result in the overactivation of systemic and renal local complement system, and increased glomerular deposition of Gd-IgA1, which may lead to renal dysfunction and promote renal progression in IgAN patients.

Diabetic kidney disease (DKD), a primary microvascular complication arising from diabetes, may result in end-stage renal disease. Epigenetic regulation of endothelial mesenchymal transition (EndMT) has been recently reported to exert function in metabolic memory and DKD. Here, we investigated the mechanism which Sirt7 modulated EndMT in human glomerular endothelial cells (HGECs) in the occurrence of metabolic memory in DKD. Lower levels of SDC1 and Sirt7 were noted in the glomeruli of both DKD patients and diabetes-induced renal injury rats, as well as in human glomerular endothelial cells (HGECs) with high blood sugar. Endothelial-to-mesenchymal transition (EndMT) was sustained despite the normalization of glycaemic control. We also found that Sirt7 overexpression associated with glucose normalization promoted the SDC1 expression and reversed EndMT in HGECs. Furthermore, the sh-Sirt7-mediated EndMT could be reversed by SDC1 overexpression. The ChIP assay revealed enrichment of Sirt7 and H3K18ac in the SDC1 promoter region. Furthermore, hypermethylated in cancer 1 (HIC1) was found to be associated with Sirt7. Overexpression of HIC1 with normoglycaemia reversed high glucose-mediated EndMT in HGECs. The knockdown of HIC1-mediated EndMT was reversed by SDC1 upregulation. In addition, the enrichment of HIC1 and Sirt7 was observed in the same promoter region of SDC1. The overexpressed Sirt7 reversed EndMT and improved renal function in insulin-treated diabetic models. This study demonstrated that the hyperglycaemia-mediated interaction between Sirt7 and HIC1 exerts a role in the metabolic memory in DKD by inactivating SDC1 transcription and mediating EndMT despite glucose normalization in HGECs.

BACKGROUND: Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consuming. Automatically quantifying glomerular morphologic features is urgently needed.
METHODS: A series of convolutional neural networks (CNN) were designed to identify and classify glomerular morphologic features in DN patients. Associations of these digital features with pathologic classification and prognosis were further analyzed.
RESULTS: Our CNN-based model achieved a 0.928 F1-score for global glomerulosclerosis and 0.953 F1-score for Kimmelstiel-Wilson lesion, further obtained a dice of 0.870 for the mesangial area and F1-score beyond 0.839 for three glomerular intrinsic cells. As the pathologic classes increased, mesangial cell numbers and mesangial area increased, and podocyte numbers decreased (p for all < 0.001), while endothelial cell numbers remained stable (p = 0.431). Glomeruli with Kimmelstiel-Wilson lesion showed more severe podocyte deletion compared to those without (p < 0.001). Furthermore, CNN-based classifications showed moderate agreement with pathologists-based classification, the kappa value between the CNN model 3 and pathologists reached 0.624 (ranging from 0.529 to 0.688, p < 0.001). Notably, CNN-based classifications obtained equivalent performance to pathologists-based classifications on predicting baseline and long-term renal function.
CONCLUSIONS: Our CNN-based model is promising in assisting the identification and pathologic classification of glomerular lesions in DN patients.

暂无摘要。

Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intensive interpretation process by renal pathologists. This paper proposes a deep convolutional neural network (CNN)-based approach to automatically detect and classify glomeruli with different stains in renal pathology images. In the glomerulus detection stage, this paper proposes a flattened Xception with a feature pyramid network (FX-FPN). The FX-FPN is employed as a backbone in the framework of faster region-based CNN to improve glomerulus detection performance. In the classification stage, this paper considers classifications of five glomerulus morphologies using a flattened Xception classifier. To endow the classifier with higher discriminability, this paper proposes a generative data augmentation approach for patch-based glomerulus morphology augmentation. New glomerulus patches of different morphologies are generated for data augmentation through the cycle-consistent generative adversarial network (CycleGAN). The single detection model shows the F1 score up to 0.9524 in H&E and PAS stains. The classification result shows that the average sensitivity and specificity are 0.7077 and 0.9316, respectively, by using the flattened Xception with the original training data. The sensitivity and specificity increase to 0.7623 and 0.9443, respectively, by using the generative data augmentation. Comparisons with different deep CNN models show the effectiveness and superiority of the proposed approach.

患者女性，36岁。临床主要表现为反复水肿、大量蛋白尿，既往肾组织穿刺活检病理诊断局灶增生性IgA肾病，予足量泼尼松（50 mg/d）联合免疫抑制剂（环磷酰胺累积剂量8.2 g）治疗，未达完全缓解。入院时24 h尿蛋白定量1.36 g，血白蛋白32 g/L，血肌酐75 μmol/L，总胆固醇6.55 mmol/L，IgG 3.56 g/L，抗核抗体H 1∶80。再次行肾组织穿刺活检病理示，轻度系膜增生性肾小球肾炎伴节段硬化，电镜示纤维样肾小球病，免疫组化示DNA-J热休克蛋白家族成员B9（DNAJB9）染色阳性，但蛋白质谱示DNAJB9阴性。诊断纤维性肾小球肾炎，继续小剂量环孢素A治疗（25 mg，每日2次），此后患者门诊规律随诊，尿蛋白逐渐减低，尿总蛋白肌酐比2 001 mg/g Cr，血肌酐86 μmol/L。纤维性肾小球肾炎是一种罕见的肾小球疾病，对治疗效果不佳的难治病例，应及时思考诊断与治疗的合理性，适当重复肾脏病理检查，为患者争取正确诊断、早期治疗的机会。.

Minimal Change Disease (MCD), which is associated with podocyte injury, is the leading cause of nephrotic syndrome in children. A considerable number of patients experience relapses and require prolonged use of prednisone and immunosuppressants. Multi-drug resistance and frequent relapses can lead to disease progression to focal and segmental glomerulosclerosis (FSGS). To identify potential targets for therapy of podocyte injury, we examined microarray data of mRNAs in glomerular samples from both MCD patients and healthy donors, obtained from the GEO database. Differentially expressed genes (DEGs) were used to construct the protein-protein interactions (PPI) network through the application of the search tool for the retrieval of interacting genes (STRING) tool. The most connected genes in the network were ranked using cytoHubba. 16 hub genes were selected and validated by qRT-PCR. RAC2 was identified as a potential therapeutic target for further investigation. By downregulating RAC2, Adriamycin (ADR)-induced human podocytes (HPCs) injury was attenuated. EHT-1864, a small molecule inhibitor that targets the RAC (RAC1, RAC2, RAC3) family, proved to be more effective than RAC2 silencing in reducing HPCs injury. In conclusion, our research suggests that EHT-1864 may be a promising new molecular drug candidate for patients with MCD and FSGS.

BACKGROUND: Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function.
METHODS: Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification.
RESULTS: This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model.
CONCLUSIONS: The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.

IgA Nephropathy (IgAN) is a disease of the glomeruli that may eventually lead to chronic kidney disease or kidney failure. The signs and symptoms of IgAN nephropathy are usually not specific enough and are similar to those of other glomerular or inflammatory diseases. This makes a correct diagnosis more difficult. This study collected data from a sample of adult patients diagnosed with primary IgAN at the First Affiliated Hospital of Wenzhou Medical University, with proteinuria ≥1 g/d at the time of diagnosis. Based on these samples, we propose a machine learning framework based on weIghted meaN oF vectOrs (INFO). An enhanced COINFO algorithm is proposed by merging INFO, Cauchy Mutation (CM) and Oppositional-based Learning (OBL) strategies. At the same time, COINFO and Support Vector Machine (SVM) were integrated to construct the BCOINFO-SVM framework for IgAN diagnosis and prediction. Initially, the proposed enhanced COINFO is evaluated using the IEEE CEC2017 benchmark problems, with the outcomes demonstrating its efficient optimization capability and accuracy in convergence. Furthermore, the feature selection capability of the proposed method is verified on the public medical datasets. Finally, the auxiliary diagnostic experiment was carried out through IgAN real sample data. The results demonstrate that the proposed BCOINFO-SVM can screen out essential features such as High-Density Lipoprotein (HDL), Uric Acid (UA), Cardiovascular Disease (CVD), Hypertension and Diabetes. Simultaneously, the BCOINFO-SVM model achieves an accuracy of 98.56%, with sensitivity at 96.08% and specificity at 97.73%, making it a potential auxiliary diagnostic model for IgAN.