Bioinformatics analysis identifies immune-related gene signatures and subtypes in diabetic nephropathy

Comprehensive Analysis of Immune Characteristics of Fluorosis and Cuprotosis-Related Genes in Fluorosis Targeted Drugs

This study aims to investigate the role of cuprotosis in fluorosis and identify potential targeted drugs for its treatment. The GSE70719 and GSE195920 datasets were merged using the inSilicoMerging package. DEGs between the exposure and control groups were found using R software. Overlapping genes of DEG and cuprotosis-related genes (CRGs) were obtained by Venn diagram and were enriched by GO and KEGG. Hub genes were identified using PPI networks and enriched by GSEA. ROC curves, the xCell algorithm, and consensus cluster analysis were utilized to evaluate diagnostic efficacy, examine immune cell infiltration, and identify cuproptosis subtypes, respectively. The GSE53937 dataset was used for external validation. The DSigDB database was used to predict small molecule drugs. Molecular docking was used to validate the relationship between small molecule drugs and hub genes. A total of 1522 DEGs (743 upregulated genes and 779 downregulated genes) and 33 overlapping genes of DEGs and CRGs were obtained. The 33 overlapping genes were enriched in ribosomal biogenesis and oxidative phosphorylation pathways. The hub genes DNTTIP2, GTPBP4, IMP4, MRPL12, MRPL13, MRPL2, MRPS2, MRPS22, NOP2, RSL1D1, and SURF6 were identified, demonstrating great diagnostic ability with AUC > 0.8. These hub genes were associated with immune response and inflammation. Two cuproptosis patterns were established based on 33 CRGs. Mepacrine was screened as a potential drug and demonstrated stability in docking with IMP4. In summary, the current study identified several CRGs that may serve as potential biomarkers for diagnosing fluorosis and are involved in fluoride-induced immune responses. Additionally, mepacrine was screened as a potential treatment for fluorosis by targeting CRGs.

Causal relationship between 731 immune cells and the risk of diabetic nephropathy: a two‑sample bidirectional Mendelian randomization study

OBJECTIVE

Previous observational studies have indicated associations between various immune cells and diabetic nephropathy (DN). However, the causality remains unclear. We aimed to further evaluate the causal association between immune cells and DN using bidirectional two-sample Mendelian randomization (MR) analysis.

METHOD

The DN data were retrieved from the IEU OpenGWAS Project database, while the data for 731 immune cells were sourced from GWAS summary statistics by Orru ̀ et al. The investigation into the causal relationship between immune cells and DN employed the inverse variance weighted (IVW), weighted median (WME), and MR-Egger methods. The stability and reliability of the findings underwent evaluation through Cochran's Q test, MR-Egger intercept's P-value, MR-PRESSO, and Leave-One-Out (LOO) method.

RESULT

The IVW estimates suggested a positive causal effect of CD25 on IgD-CD38dim B cell, CD25 on naive-mature B cell, CD127 on granulocyte, SSC-A on HLA DR + Natural Killer, HLA DR on plasmacytoid Dendritic Cell, and HLA DR on Dendritic Cell on DN. Conversely, the abundance of Myeloid Dendritic Cell, CD62L- Dendritic Cell %Dendritic Cell, CD86+ myeloid Dendritic Cell %Dendritic Cell, CD14- CD16-, CX3CR1 on CD14- CD16-, and SSC-A on CD4+ T cell had negative causal effects on DN. However, after correcting the P value for significant causality results using the FDR method, it was concluded that only Myeloid Dendritic Cells had a causal relationship with DN (FDR-p = 0.041), while the other immune cells showed no significant association with DN, so their relationship was suggestive. The results were stable with no observed horizontal pleiotropy and heterogeneity. Reverse MR analysis indicated no causal relationship between DN and the increased risk of positively identified immune cells.

CONCLUSION

This study provides an initial insight into the genetic perspective of the causal relationship between immune cells and DN. It establishes a crucial theoretical foundation for future endeavors in precision medicine and individualized treatment.

Pathway of LCK Tyrosine Kinase and mTOR Signaling in Children with T-Cell Acute Lymphoblastic Leukemia

The aim of this study is to analyze available research on targeting signaling pathways for the development of new drugs in patients with T-cell acute lymphoblastic leukemia (T-ALL). This analysis focuses specifically on the role of LCK tyrosine kinase and mTOR signaling pathways in pediatric patients. Outcome: Current literature suggests that these pathways play a significant role in the regulation of T-cell cycles, making them potential therapeutic targets. However, despite promising findings, there remains a need for further research, particularly in pediatric populations, to fully understand the therapeutic implications and to optimize drug development. The conclusion drawn from this analysis highlights the significant influence of LCK and mTOR on T-cell cycle regulation, underscoring the importance of continued investigation in this area.

The advance of single cell transcriptome to study kidney immune cells in diabetic kidney disease

Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and a primary cause of end-stage renal disease (ESRD). Increasing studies suggest that immune cells are involved in regulating renal inflammation, which contributes to the progression of DKD. Compared with conventional methods, single-cell sequencing technology is more developed technique that has advantages in resolving cellular heterogeneity, parallel multi-omics studies, and discovering new cell types. ScRNA-seq helps researchers to analyze specifically gene expressions, signaling pathways, intercellular communication as well as their regulations in various immune cells of kidney biopsy and urine samples. It is still challenging to investigate the function of each cell type in the pathophysiology of kidney due to its complex and heterogeneous structure and function. Here, we discuss the application of single-cell transcriptomics in the field of DKD and highlight several recent studies that explore the important role of immune cells including macrophage, T cells, B cells etc. in DKD through scRNA-seq analyses. Through combing the researches of scRNA-seq on immune cells in DKD, this review provides novel perspectives on the pathogenesis and immune therapeutic strategy for DKD.

Relationship between immune cells and diabetic nephropathy: a Mendelian randomization study

OBJECTIVE

Although previous studies have suggested potential correlations between immunocytes and diabetic nephropathy (DN), the causal correlations between them remain unclarified. In this study, we employed Mendelian randomization (MR) to analyze the potential causative correlations between immune 731 cells and DN.

METHODS

We used the Genome-Wide Association Studies (GWAS) database to aggregate signatures of immune cells and DN from European and East Asian populations. Single-nucleotide polymorphisms (SNPs) were used as instrumental variables. MR analysis was conducted using Mendelian randomization-Egger (MR-Egger) regression and the random-effects inverse-variance weighted (IVW) method.

RESULTS

A total of 3,571 SNPs were included as instrumental variables. The MR-Egger regression model indicated no genetic pleiotropy (P = 0.6284). The results of the IVW method indicated a statistically significant causal relationship between immune cell HLA-DR on CD14-CD16- (P = 0.029), CD45RA-CD28-CD8 + T cell% T cells (P = 0.0278), CD11c on myeloid dendritic cells (P = 0.0352), HLA-DR on CD14+ monocytes (P < 0.001), CD27 on CD24 + CD27 + B cells (P = 0.0334), CD27 on IgD + CD24 + B cells (P = 0.0137), CD4 on CD39 + CD4 + T cells (P = 0.0347), CD28 on CD39 + CD4 + T cells (P = 0.0414), CD39 on CD39 + CD4 + T cells (P = 0.0426), and DN. Additionally, there was no heterogeneity in SNPs related HLA-DR on CD14-CD16-cells and DN(I2 = 32%, Cochran's Q = 2.9476, P = 0.2291). Moreover, leave-one-out analysis showed a causal correlation between HLA-DR on CD14-CD16- cells and DN.

CONCLUSION

Higher expression of immune cell HLA-DR on CD14-CD16- cells may indicate a lower risk of DN.

Causal role of immune cells in diabetic nephropathy: a bidirectional Mendelian randomization study

Background

Diabetic nephropathy (DN) stands as a pervasive chronic renal disease worldwide, emerging as the leading cause of renal failure in end-stage renal disease. Our objective is to pinpoint potential immune biomarkers and evaluate the causal effects of prospective therapeutic targets in the context of DN.

Methods

We employed Mendelian randomization (MR) analysis to examine the causal associations between 731 immune cell signatures and the risk of DN. Various analytical methods, including inverse-variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode, were employed for the analysis. The primary analytical approach utilized was the inverse-variance weighted (IVW) method. To ensure the reliability of our findings, we conducted comprehensive sensitivity analyses to assess the robustness, heterogeneity, and presence of horizontal pleiotropy in the results. Statistical powers were also calculated. Ultimately, a reverse Mendelian randomization (MR) analysis was conducted to assess the potential for reverse causation.

Results

After Benjamini & Hochberg (BH) correction, four immunophenotypes were identified to be significantly associated with DN risk: HLA DR on Dendritic Cell (OR=1.4460, 95% CI = 1.2904~1.6205, P=2.18×10-10, P.adjusted= 1.6×10-7), HLA DR on CD14+ CD16- monocyte (OR=1.2396, 95% CI=1.1315~1.3580, P=3.93×10-6, P.adjusted = 0.00143). HLA DR on CD14+ monocyte (OR=1.2411, 95% CI=1.12957~1.3637, P=6.97×10-6, P.adjusted=0.0016), HLA DR on plasmacytoid Dendritic Cell (OR=1.2733, 95% CI= 1.1273~1.4382, P= 0.0001, P.adjusted = 0.0183). Significant heterogeneity of instrumental variables was found in the four exposures, and significant horizontal pleiotropy was only found in HLA DR on Dendritic Cell. The bidirectional effects between the immune cells and DN were not supported.

Conclusion

Our research illustrated the intimate association between immune cells and DN, which may contribute to a deeper understanding of the intricate mechanisms underlying DN and aid in the identification of novel intervention target pathways.

VCAM1: an effective diagnostic marker related to immune cell infiltration in diabetic nephropathy

Introduction

The role of immune cells in the pathogenesis and advancement of diabetic nephropathy (DN) is crucial. The objective of this study was to identify immune-cell-related biomarkers that could potentially aid in the diagnosis and management of DN.

Methods

The GSE96804 dataset was obtained from the Gene Expression Omnibus (GEO) database. Then, screen for intersections between differentially expressed genes (DEGs) and immune-related genes (IRGs). Identify core genes through protein-protein interaction (PPI) networks and the Cytoscape plugin. Subsequently, functional enrichment analysis was conducted. In addition, ROC analysis is performed to accurately identify diagnostic biomarkers. Apply the CIBERSORT algorithm to evaluate the proportion of immune cell infiltration. Finally, the mRNA, protein, and immunofluorescence expression of the biomarker was validated in the DN rat model.

Results

The study yielded 74 shared genes associated with DN. Enrichment analysis indicated significant enrichment of these genes in focal adhesion, the humoral immune response, activation of the immune response, Cytokine-cytokine receptor interaction, and IL-17 signaling pathway. The optimal candidate gene VCAM1 was identified. The presence of VCAM1 in DN was further validated using the ROC curve. Analysis of immune cell infiltration matrices revealed a high abundance of monocytes, naïve B cells, memory B cells, and Macrophages M1/M2 in DN tissues. Correlation analysis identified one hub biomarker associated with immune-infiltrated cells in DN. Furthermore, our findings were validated through in vivo RT qPCR, WB, and IF techniques.

Conclusions

Our research indicates that VCAM1 is a signature gene associated with DN and is linked to the progression, treatment, and prognosis of DN. A comprehensive examination of immune infiltration signature genes may offer new perspectives on the clinical diagnosis and management of DN.

The Effect of Vitamin D Deficiency on Immune-Related Hub Genes: A Network Analysis Associated With Type 1 Diabetes

Background Type 1 diabetes (T1D) is an autoimmune disorder that results in the destruction of pancreatic beta cells, causing a shortage of insulin secretion. The development of T1D is influenced by both genetic predisposition and environmental factors, such as vitamin D. This vitamin is known for its ability to regulate the immune system and has been associated with a decreased risk of T1D. However, the specific ways in which vitamin D affects immune regulation and the preservation of beta cells in T1D are not yet fully understood. Gaining a better understanding of these interactions is essential for identifying potential targets for preventing and treating T1D. Methods The analysis focused on two Gene Expression Omnibus (GEO) datasets, namely, GSE55098 and GSE50012, to detect differentially expressed genes (DEGs). Enrichr (Ma'ayan Laboratory, New York, NY) was used to perform enrichment analysis for the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The Search Tool for the Retrieval of Interacting Genes 12.0 (STRING) database was used to generate a protein-protein interaction (PPI) network. The Cytoscape 3.10.1 (Cytoscape Team, San Diego, CA) was used to analyze the PPI network and discover the hub genes. Results The DEGs in both datasets were identified using the GEO2R tool, with a particular focus on genes exhibiting contrasting regulations. Enrichment analysis unveiled the participation of these oppositely regulated DEGs in processes relevant to the immune system. Cytoscape analysis of the PPI network revealed five hub genes, MNDA, LILRB2, FPR2, HCK, and FCGR2A, suggesting their potential role in the pathogenesis of T1D and the response to vitamin D. Conclusion The study elucidates the complex interaction between vitamin D metabolism and immune regulation in T1D. The identified hub genes provide important knowledge on the molecular pathways that underlie T1D and have the potential to be targeted for therapeutic intervention. This research underscores the importance of vitamin D in the immune system's modulation and its impact on T1D development.

Pathological mechanism of immune disorders in diabetic kidney disease and intervention strategies

Diabetic kidney disease is one of the most severe chronic microvascular complications of diabetes and a primary cause of end-stage renal disease. Clinical studies have shown that renal inflammation is a key factor determining kidney damage during diabetes. With the development of immunological technology, many studies have shown that diabetic nephropathy is an immune complex disease, and that most patients have immune dysfunction. However, the immune response associated with diabetic nephropathy and autoimmune kidney disease, or caused by ischemia or infection with acute renal injury, is different, and has a com-plicated pathological mechanism. In this review, we discuss the pathogenesis of diabetic nephropathy in immune disorders and the intervention mechanism, to provide guidance and advice for early intervention and treatment of diabetic nephropathy.

The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies

Diabetic kidney disease (DKD), is a common microvascular complication and a major cause of death in patients with diabetes. Disorders of immune cells and immune cytokines can accelerate DKD development of in a number of ways. As the kidney is composed of complex and highly differentiated cells, the interactions among different cell types and immune cells play important regulatory roles in disease development. Here, we summarize the latest research into the molecular mechanisms underlying the interactions among various immune and renal cells in DKD. In addition, we discuss the most recent studies related to single cell technology and bioinformatics analysis in the field of DKD. The aims of our review were to explore immune cells as potential therapeutic targets in DKD and provide some guidance for future clinical treatments.

Identification of potential immunotherapeutic targets and prognostic biomarkers in Graves' disease using weighted gene co-expression network analysis

Graves' disease (GD) is an autoimmune disorder characterized by hyperthyroidism resulting from autoantibody-induced stimulation of the thyroid gland. Despite recent advancements in understanding GD's pathogenesis, the molecular processes driving disease progression and treatment response remain poorly understood. In this study, we aimed to identify crucial immunogenic factors associated with GD prognosis and immunotherapeutic response. To achieve this, we implemented a comprehensive screening strategy that combined computational immunogenicity-potential scoring with multi-parametric cluster analysis to assess the immunomodulatory genes in GD-related subtypes involving stromal and immune cells. Utilizing weighted gene co-expression network analysis (WGCNA), we identified co-expressed gene modules linked to cellular senescence and immune infiltration in CD4+ and CD8+ GD samples. Additionally, gene set enrichment analysis enabled the identification of hallmark pathways distinguishing high- and low-immune subtypes. Our WGCNA analysis revealed 21 gene co-expression modules comprising 1,541 genes associated with immune infiltration components in various stages of GD, including T cells, M1 and M2 macrophages, NK cells, and Tregs. These genes primarily participated in T cell proliferation through purinergic signaling pathways, particularly neuroactive ligand-receptor interactions, and DNA binding transcription factor activity. Three genes, namely PRSS1, HCRTR1, and P2RY4, exhibited robustness in GD patients across multiple stages and were involved in immune cell infiltration during the late stage of GD (p < 0.05). Importantly, HCRTR1 and P2RY4 emerged as potential prognostic signatures for predicting overall survival in high-immunocore GD patients (p < 0.05). Overall, our study provides novel insights into the molecular mechanisms driving GD progression and highlights potential key immunogens for further investigation. These findings underscore the significance of immune infiltration-related cellular senescence in GD therapy and present promising targets for the development of new immunotherapeutic strategies.

Single-cell and transcriptome analysis reveals TAL cells in diabetic nephropathy

Diabetic nephropathy is a global public health concern with multifaceted pathogenesis, primarily involving hypertension. Excessive activation of AT1R has been strongly associated with hypertension onset and progression in diabetic nephropathy. This study aimed to conduct thick ascending limb cell single-cell and transcriptomic analysis in diabetic nephropathy, including screening for biological markers, cellular communication, and immune infiltration, to identify potential biomarkers and effective means for prevention and treatment. By using high-dimensional weighted gene co-expression network analysis, least absolute shrinkage and selection operator, machine learning, neural deconvolution, quasi-chronological analysis, non-negative matrix factorization clustering, and monocyte chemotactic protein-induced counter, we identified 7 potential thick ascending limb cell biomarkers for diabetic nephropathy and elucidated the bone morphogenetic protein pathway's regulation of thick ascending limb cells through podocyte epithelial cells and podocyte cells. The study also highlighted the role of COBL, PPARGC1A, and THSD7A in non-negative matrix factorization clustering and their relationship with thick ascending limb cell immunity in diabetic nephropathy. Our findings provide new insights and avenues for managing diabetic nephropathy, ultimately alleviating the burden on patients and society.

Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis

Background

Diabetic nephropathy (DN), the most intractable complication in diabetes patients, can lead to proteinuria and progressive reduction of glomerular filtration rate (GFR), which seriously affects the quality of life of patients and is associated with high mortality. However, the lack of accurate key candidate genes makes diagnosis of DN very difficult. This study aimed to identify new potential candidate genes for DN using bioinformatics, and elucidated the mechanism of DN at the cellular transcriptional level.

Methods

The microarray dataset GSE30529 was downloaded from the Gene Expression Omnibus Database (GEO), and the differentially expressed genes (DEGs) were screened by R software. We used Gene Ontology (GO), gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify the signal pathways and genes. Protein-protein interaction (PPI) networks were constructed using the STRING database. The GSE30122 dataset was selected as the validation set. Receiver operating characteristic (ROC) curves were applied to evaluate the predictive value of genes. An area under curve (AUC) greater than 0.85 was considered to be of high diagnostic value. Several online databases were used to predict miRNAs and transcription factors (TFs) capable of binding hub genes. Cytoscape was used for constructing a miRNA-mRNA-TF network. The online database 'nephroseq' predicted the correlation between genes and kidney function. The serum level of creatinine, BUN, and albumin, and the urinary protein/creatinine ratio of the DN rat model were detected. The expression of hub genes was further verified through qPCR. Data were analyzed statistically using Student's t-test by the 'ggpubr' package.

Results

A total of 463 DEGs were identified from GSE30529. According to enrichment analysis, DEGs were mainly enriched in the immune response, coagulation cascades, and cytokine signaling pathways. Twenty hub genes with the highest connectivity and several gene cluster modules were ensured using Cytoscape. Five high diagnostic hub genes were selected and verified by GSE30122. The MiRNA-mRNA-TF network suggested a potential RNA regulatory relationship. Hub gene expression was positively correlated with kidney injury. The level of serum creatinine and BUN in the DN group was higher than in the control group (unpaired t test, t = 3.391, df = 4, p = 0.0275, r = 0.861). Meanwhile, the DN group had a higher urinary protein/creatinine ratio (unpaired t test, t = 17.23, df = 16, p < 0.001, r = 0.974). QPCR results showed that the potential candidate genes for DN diagnosis included C1QB, ITGAM, and ITGB2.

Conclusions

We identified C1QB, ITGAM and ITGB2 as potential candidate genes for DN diagnosis and therapy and provided insight into the mechanisms of DN development at transcriptome level. We further completed the construction of miRNA-mRNA-TF network to propose potential RNA regulatory pathways adjusting disease progression in DN.

Chinese herbal medicine and its active compounds in attenuating renal injury via regulating autophagy in diabetic kidney disease

Diabetic kidney disease (DKD) is the main cause of end-stage renal disease worldwide, and there is a lack of effective treatment strategies. Autophagy is a highly conserved lysosomal degradation process that maintains homeostasis and energy balance by removing protein aggregates and damaged organelles. Increasing evidence suggests that dysregulated autophagy may contribute to glomerular and tubulointerstitial lesions in the kidney under diabetic conditions. Emerging studies have shown that Chinese herbal medicine and its active compounds may ameliorate diabetic kidney injury by regulating autophagy. In this review, we summarize that dysregulation or insufficiency of autophagy in renal cells, including podocytes, glomerular mesangial cells, and proximal tubular epithelial cells, is a key mechanism for the development of DKD, and focus on the protective effects of Chinese herbal medicine and its active compounds. Moreover, we systematically reviewed the mechanism of autophagy in DKD regulated by Chinese herb compound preparations, single herb and active compounds, so as to provide new drug candidates for clinical treatment of DKD. Finally, we also reviewed the candidate targets of Chinese herbal medicine regulating autophagy for DKD. Therefore, further research on Chinese herbal medicine with autophagy regulation and their targets is of great significance for the realization of new targeted therapies for DKD.

Integrative analyses of biomarkers and pathways for diabetic nephropathy

Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world's populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown. Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN. Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes (a total of 120 DEGs). GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) were finally identified as having diagnostic values. Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN.