Exploring macrophage heterogeneity in IgA nephropathy: Mechanisms of renal impairment and current therapeutic targets.

The lack of understanding of the mechanism of renal injury in IgA nephropathy (IgAN) hinders the development of personalized treatment plans and targeted therapies. Improved insight into the cause of renal dysfunction in IgAN is necessary to enhance the effectiveness of strategies for slowing the progression of the disease. This study examined single cell RNA sequencing (scRNA seq) and bulk-RNA seq data and found that the gene expression of renal intrinsic cells (RIC) was significantly changed in patients with renal impairment, with a primary focus on energy metabolism. We discovered a clear metabolic reprogramming of RIC during renal function impairment (RF) using the 'scMetabolism' package, which manifested as a weakening of oxidative phosphorylation, alterations in fatty acid metabolism, and changes in glycolysis. Cellular communication analysis revealed that communication between macrophages (Ma) and RIC became more active and impacted cell function through the ligand-receptor-transcription factor (L-R-TF) axis in patients with RF. Our studies showed a notable upsurge in the expression of gene CLU and the infiltration of CLU+ Ma in patients with RF. CLU is a multifunctional protein, extensively involved in processes such as cell apoptosis and immune responses. Data obtained from the Nephroseq V5 database and multiplex immunohistochemistry (mIHC) were used to validate the findings, which were found to be robustly correlated with estimated glomerular filtration rate (eGFR) of the IgAN patients, as demonstrated by linear regression (LR). This study provides new insights into the cellular and molecular changes that occur in IgAN during renal impairment, revealing that elevated expression of CLU and CLU+ Ma percolation are common features in patients with RF. These findings offer potential targets and strategies for personalized management and targeted therapy of IgAN.

Dual Anti-Glomerular Basement Membrane and Anti-Neutrophil Cytoplasmic Antibodies-Positive Rapidly Progressive Glomerulonephritis with Rheumatoid Arthritis and Sjogren's Syndrome: A Case Report and Literature Review.

Rapidly progressive glomerulonephritis (RPGN) is a life-threatening disease characterized by rapid progressive deterioration of renal function and extensive formation of crescents. Some antibodies tend to be positive, such as a perinuclear anti-neutrophil cytoplasmic antibody (p-ANCA) and anti-glomerular basement membrane (anti-GBM) antibodies, in most patients with the disease. However, cases of double positivity for the above antibodies are considered to be rare. In addition, both rheumatoid arthritis (RA) and Sjogren's syndrome (SS) are deemed to be independent immune disorders that can cause renal impairment. Nevertheless, the association between RPGN and these two diseases has not been elucidated in previous studies. Here, we provide a case of RPGN with the concurrence of RA and SS characterized by double positivity in anti-GBM antibodies and p-ANCA. After aggressive treatment with cyclophosphamide, glucocorticoids, and plasma exchange, the patient improved significantly. Despite the malignant event of arteriovenous fistula rupture and bleeding during treatment, the patient survived with renal function recovery for the rest of the follow-up period.

Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning.

(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression profiles of eight GN subtypes. Then, differentially expressed immune-related genes (DIRGs) were identified to explore the molecular mechanisms of GN, and single-sample gene set enrichment analysis (ssGSEA) was performed to discover the abnormal inflammation in GN. In addition, a nomogram model was generated using the R package "glmnet", and the calibration curve was plotted to evaluate the predictive power of the nomogram model. Finally, deep learning (DL) based on a multilayer perceptron (MLP) network was performed to explore the characteristic genes for GN. (3) Results: we screened out 274 common up-regulated or down-regulated DIRGs in the glomeruli and tubulointerstitium. These DIRGs are mainly involved in T-cell differentiation, the RAS signaling pathway, and the MAPK signaling pathway. ssGSEA indicates that there is a significant increase in DC (dendritic cells) and macrophages, and a significant decrease in neutrophils and NKT cells in glomeruli, while monocytes and NK cells are increased in tubulointerstitium. A nomogram model was constructed to predict GN based on 7 DIRGs, and 20 DIRGs of each subtype of GN in glomeruli and tubulointerstitium were selected as characteristic genes. (4) Conclusions: this study reveals that the DIRGs are closely related to the pathogenesis of GN and could serve as genetic biomarkers in GN. DL further identified the characteristic genes that are essential to define the pathogenesis of GN and develop targeted therapies for eight GN subtypes.