Identification of copper-related biomarkers and potential molecule mechanism in diabetic nephropathy

DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure

Introduction

Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). However, there is still a lack of effective and reliable HF diagnostic markers and therapeutic targets for patients with MHD.

Methods

In this study, we analyzed transcriptome profiles of 30 patients with MHD by high-throughput sequencing. Firstly, the differential genes between HF group and control group of patients with MHD were screened. Secondly, HF-related genes were screened by WGCNA, and finally the genes intersecting the two were selected as candidate genes. Machine learning was used to identify hub gene and construct a nomogram model, which was verified by ROC curve and RT-qPCR. In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.

Results

Totally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. Through GSEA analysis, it was found that the four genes were closely related to ribosome, cell cycle, ubiquitin-mediated proteolysis. We constructed a ceRNA regulatory network, and found that 4 hub genes (TYMS, CDCA2 and DEPDC1B) might be regulated by 4 miRNAs (hsa-miR-1297, hsa-miR-4465, hsa-miR-27a-3p, hsa-miR-129-5p) and 21 lncRNAs (such as HCP5, CAS5, MEG3, HCG18). 24 small molecule drugs were predicted based on TYMS through DrugBank website. Finally, qRT-PCR experiments showed that the expression trend of biomarkers was consistent with the results of transcriptome sequencing.

Discussion

Overall, our results reveal the molecular mechanism of HF in patients with MHD and provide insights into potential diagnostic markers and therapeutic targets.

In-depth investigation of the complex pathophysiological mechanisms between diabetes and ischemic stroke through gene expression and regulatory network analysis

This study explores the intricate relationship between diabetes and ischemic stroke (IS) through gene expression analysis and regulatory network investigation to identify potential biomarkers and therapeutic targets. Using datasets from the Gene Expression Omnibus (GEO) database, differential gene analysis was conducted on GSE43950 (diabetes) and GSE16561 (IS), revealing overlapping differentially expressed genes (DEGs). Functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, and hub gene identification were performed, followed by validation in independent datasets (GSE156035 and GSE58294). The analysis identified 307 upregulated and 156 downregulated overlapping DEGs with significant enrichment in GO and KEGG pathways. Key hub genes (TLR2, TLR4, HDAC1, ITGAM) were identified through a PPI network (257 nodes, 456 interactions), with their roles in immune and inflammatory responses highlighted through GeneMANIA analysis. TRRUST-based transcription factor enrichment analysis revealed regulatory links involving RELA, SPI1, STAT3, and SP1. Differential expression analysis confirmed that RELA and SPI1 were upregulated in diabetes, while SPI1, STAT3, and SP1 were linked to IS. These transcription factors are involved in regulating immunity and inflammation, providing insights into the molecular mechanisms underlying diabetes-IS comorbidity. This bioinformatics-driven approach offers new understanding of the gene interactions and pathways involved, paving the way for potential therapeutic targets.

Cell Division Cycle 42 Improves Renal Functions, Fibrosis, Th1/Th17 Infiltration and Inflammation to Some Degree in Diabetic Nephropathy

Our two previous studies observed that cell division cycle 42 (CDC42) was lower and correlated with improved renal function and inflammation in diabetic nephropathy (DN) patients, and CDC42 inhibited renal tubular epithelial cell fibrosis and inflammation under high glucose condition. Sequentially, this current study aimed to investigate the effect of CDC42 on improving renal function, fibrosis, and inflammation in DN mice, and its interaction with T cell receptor (TCR) related pathways. Mice were treated by streptozotocin to construct early-stage DN model, then transfected with CDC42 overexpression adenovirus, followed by simultaneous treatment of LY294002 (PI3K/AKT inhibitor) and CI-1040 (ERK inhibitor), respectively. CDC42 reduced blood glucose, creatinine, and 24 h urine protein in DN mice, but only showed a tendency to decrease blood urea nitrogen without statistical significance. Hematoxylin&eosin staining revealed that CDC42 descended the glomerular volume, basement membrane thickness, and inflammatory cell infiltration in kidney. Meanwhile, CDC42 lowered fibronectin, TGF-β1, and Collagen I expressions in kidney, but not decreased α-SMA significantly. Besides, CDC42 decreased T-helper (Th) 1 and Th17 cells in kidney, and reduced serum IFN-γ, IL-1β, IL-17A, and TNF-α but not IL-6. Regarding TCR-related pathways, CDC42 activated AKT and ERK pathways but not JNK pathway. However, the treatment of LY294002 and CI-1040 had limited effect on attenuating CDC42's functions on renal function and fibrotic markers. CDC42 improves renal functions, fibrosis, Th1/Th17 infiltration and inflammation to some degree in DN mice, these functions may be independent to AKT and ERK pathways.

Effect of cuproptosis on acute kidney injury after cardiopulmonary bypass in diabetic patients

BACKGROUND

Cardiopulmonary bypass (CPB) is a common procedure in cardiac surgery. CPB is a high-risk factor for acute kidney injury (AKI), and diabetes is also such a factor. Diabetes can lead to copper overload. It is currently unclear whether AKI after CPB in diabetic patients is related to copper overload.

AIM

To explore whether the occurrence of CPB-AKI in diabetic patients is associated with cuproptosis.

METHODS

Blood and urine were collected from clinical diabetic and non-diabetic patients before and after CPB. Levels of copper ion, lactate, glucose, heat shock protein-70 (HSP-70), and dihydrolipoamide dehydrogenase (DLAT) were determined. A diabetic rat model was established and CPB was performed. The rats were assessed for the development of CPB-AKI, and for the association of AKI with cuproptosis by detecting copper levels, iron-sulfur cluster proteins and observation of mitochondrial structure by electron microscopy.

RESULTS

CPB resulted in elevations of copper, lactate, HSP-70 and DLAT in blood and urine in both diabetic and non-diabetic patients. CPB was associated with pathologic and mitochondrial damage in the kidneys of diabetic rats. Cuproptosis-related proteins also appeared to be significantly reduced.

CONCLUSION

CPB-AKI is associated with cuproptosis. Diabetes mellitus is an important factor aggravating CPB-AKI and cuproptosis.

AGTR1: a potential biomarker associated with the occurrence and prognosis of lung adenocarcinoma

Introduction

Lung adenocarcinoma, a disease with complex pathogenesis, high mortality and poor prognosis, is one of the subtypes of lung cancer. Hence, it is very crucial to find novel biomarkers as diagnostic and therapeutic targets for LUAD.

Methods

GSE10072 was used for DEGs and WGCNA, and the intersection genes were subjected to enrichment analysis through Metascape and GSEA. Key genes were screened by three machine learning methods. Further, the reliability of key genes was identified by ROC, COX regression analysis and qRT-PCR. CIBERSORT and Spearman analysis were used for understanding the relationships of LUAD, immunity and key genes. In addition, ceRNA networks and potential drugs of key genes were constructed and predicted.

Results

After overlapping 631 DEGs and key module genes, 623 intersection genes were obtained. Subsequently, DUOX1, CD36, AGTR1, FHL5 and SSR4 were further selected using three machine learning methods. Reliability analysis demonstrated that AGTR1 possesses important predictive value for the occurrence and prognosis of LUAD. The enrichment analysis showed that AGTR1 was significantly enriched in the GPCR-related pathways. Immune infiltration analysis showed that the development of LUAD was related to the changes of immune cells such as M2 macrophages and neutrophils, which were regulated by AGTR1. Further, AGTR1 is also involved in regulating immune chemokines, checkpoints and immune regulatory factors such as PECAM1, ADARB1, SPP1 and ENO1, all of them playing important roles in immune cell regulation, tumor cell proliferation and migration. Further, the drug-gene interaction network screened out 13 potential drugs such as Benazepril, Valsartan, Eprosartan, and so on.

Discussion

AGTR1 is a potential biomarker for the occurrence and progression of LUAD, closely related to tumor immunity, proliferation and migration. It can serve as a new target for the diagnosis and treatment of LUAD.

DNMT1-Mediated the Downregulation of FOXF1 Promotes High Glucose-induced Podocyte Damage by Regulating the miR-342-3p/E2F1 Axis

Podocyte damage plays a crucial role in the occurrence and development of diabetic nephropathy (DN). Accumulating evidence suggests that dysregulation of transcription factors plays a crucial role in podocyte damage in DN. However, the biological functions and underlying mechanisms of most transcription factors in hyperglycemia-induced podocytes damage remain largely unknown. Through integrated analysis of data mining, bioinformatics, and RT-qPCR validation, we identified a critical transcription factor forkhead box F1 (FOXF1) implicated in DN progression. Moreover, we discovered that FOXF1 was extensively down-regulated in renal tissue and serum from DN patients as well as in high glucose (HG)-induced podocyte damage. Meanwhile, our findings showed that FOXF1 might be a viable diagnostic marker for DN patients. Functional experiments demonstrated that overexpression of FOXF1 strikingly enhanced proliferation, outstandingly suppressed apoptosis, and dramatically reduced inflammation and fibrosis in HG-induced podocytes damage. Mechanistically, we found that the downregulation of FOXF1 in HG-induced podocyte damage was caused by DNMT1 directly binding to FOXF1 promoter and mediating DNA hypermethylation to block FOXF1 transcriptional activity. Furthermore, we found that FOXF1 inhibited the transcriptional expression of miR-342-3p by binding to the promoter of miR-342, resulting in reduced sponge adsorption of miR-342-3p to E2F1, promoting the expression of E2F1, and thereby inhibiting HG-induced podocytes damage. In conclusion, our findings showed that blocking the FOXF1/miR-342-3p/E2F1 axis greatly alleviated HG-induced podocyte damage, which provided a fresh perspective on the pathogenesis and therapeutic strategies for DN patients.

Cellular senescence-Related genes define the immune microenvironment and molecular characteristics in severe asthma patients

Recent studies have shown that cellular senescence is involved in the pathogenesis of severe asthma (SA). The objective of this study was to investigate the role of cellular senescence-related genes (CSGs) in the pathogenesis of SA. Here, 54 differentially expressed CSGs were identified in SA patients compared to healthy control individuals. Among the 54 differentially expressed CSGs, 3 CSGs (ETS2, ETS1 and AURKA) were screened using the LASSO regression analysis and logistic regression analysis to establish the CSG-based prediction model to predict severe asthma. Moreover, we found that the protein expression levels of ETS2, ETS1 and AURKA were increased in the severe asthma mouse model. Then, two distinct senescence subtypes of SA with distinct immune microenvironments and molecular biological characteristics were identified. Cluster 1 was characterized by increased infiltration of immature dendritic cells, regulatory T cells, and other cells. Cluster 2 was characterized by increased infiltration levels of eosinophils, neutrophils, and other cells. The molecular biological characteristics of Cluster 1 included aerobic respiration and oxidative phosphorylation, whereas the molecular biological characteristics of Cluster 2 included activation of the immune response and immune receptor activity. Then, we established an Random Forest model to predict the senescence subtypes of SA to guide treatment. Finally, potential drugs were searched for each senescence subgroup of SA patients via the Connectivity Map database. A peroxisome proliferator-activated receptor agonist may be a potential therapeutic drug for patients in Cluster 1, whereas a tachykinin antagonist may be a potential therapeutic drug for patients in Cluster 2. In summary, CSGs are likely involved in the pathogenesis of SA, which may lead to new therapeutic options for SA patients.

Serum biomarkers for predicting microvascular complications of diabetes mellitus

INTRODUCTION

Diabetic microvascular complications such as retinopathy, nephropathy, and neuropathy are primary causes of blindness, terminal renal failure, and neuropathic disorders in type 2 diabetes mellitus patients. Identifying reliable biomarkers promptly is pivotal for early detection and intervention in these severe complications.

AREAS COVERED

This review offers a thorough examination of the latest research concerning serum biomarkers for the prediction and assessment of diabetic microvascular complications. It encompasses biomarkers associated with glycation, oxidative stress, inflammation, endothelial dysfunction, basement membrane thickening, angiogenesis, and thrombosis. The review also highlights the potential of emerging biomarkers, such as microRNAs and long non-coding RNAs.

EXPERT OPINION

Serum biomarkers are emerging as valuable tools for the early assessment and therapeutic guidance of diabetic microvascular complications. The biomarkers identified not only reflect the underlying pathophysiology but also align with the extent of the disease. However, further validation across diverse populations and improvement of the practicality of these biomarkers in routine clinical practice are necessary. Pursuing these objectives is essential to advance early diagnosis, risk assessment, and individualized treatment regimens for those affected by diabetes.

The Effect of Selected Nitric Oxide Synthase Polymorphisms on the Risk of Developing Diabetic Nephropathy

BACKGROUND

Nitric oxide synthase (NOS) is an enzyme that catalyzes the formation of nitric oxide (NO), the altered production of which is characteristic of diabetic nephropathy. NOS exists in three isoforms: NOS1, NOS2, and NOS3. Moreover, there are reports about the potential role of NOS3 polymorphisms in the development of diabetes complications. The aim of this study was to assess the role of selected NOS polymorphisms-rs3782218 (NOS1), rs1137933 (NOS2), rs1799983, rs2070744, and rs61722009 (NOS3)-in the risk of developing diabetic nephropathy and in the likelihood of renal replacement therapy.

METHODS

The studied polymorphisms were analyzed in a group of 232 patients divided into three groups. Four polymorphisms (rs3782218, rs1137933, rs1799983, rs2070744) were genotyped using the PCR-RFLP, while the rs61722009 polymorphism was genotyped using the PCR.

RESULTS

The C/C genotype and the C allele of the rs3782218 polymorphism (NOS1) were associated with an increased risk of developing diabetic nephropathy and an increased likelihood of renal replacement therapy. In turn, the G allele of the rs1137933 polymorphism (NOS2) reduces the likelihood of renal replacement therapy.

CONCLUSIONS

The specific genotypes or alleles of the rs3782218 (NOS1) and rs1137933 (NOS2) polymorphisms seem to be potential risk factors for diabetic nephropathy and renal replacement therapy.

Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation

OBJECTIVE

Although programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conneted, the interplay among various PCD forms remains elusive. In this study, We aimed at identifying independently DN-associated PCD pathways and biomarkers relevant to the related pathogenesis.

METHODS

We acquired DN-related datasets from the GEO database and identified PCDs independently correlated with DN (DN-PCDs) through single-sample Gene Set Enrichment Analysis (ssGSEA) as well as, univariate and multivariate logistic regression analyses. Subsequently, applying differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz cluster analysis, we filtered the DN-PCDs pertinent to DN onset and progression. The convergence of various machine learning techniques ultimately spotlighted hub genes, substantiated through dataset meta-analyses and experimental validations, thereby confirming hub genes and related pathways expression consistencies.

RESULTS

We harmonized four DN-related datasets (GSE1009, GSE142025, GSE30528, and GSE30529) post-batch-effect removal for subsequent analyses. Our differential expression analysis yielded 709 differentially expressed genes (DEGs), comprising 446 upregulated and 263 downregulated DEGs. Based on our ssGSEA as well as univariate and multivariate logistic regressions, apoptosis and NETotic cell death were appraised as independent risk factors for DN (Odds Ratio > 1, p < 0.05). Next, we further refined 588 apoptosis- and NETotic cell death-associated genes through WGCNA and Mfuzz analysis, resulting in the identification of 17 DN-PCDs. Integrating protein-protein interaction (PPI) network analyses, network topology, and machine learning, we pinpointed hub genes (e.g., IL33, RPL11, and CX3CR1) as significant DN risk factors with expression corroborating in subsequent meta-analyses and experimental validations. Our GSEA enrichment analysis discerned differential enrichments between DN and control samples within pathways such as IL2/STAT5, IL6/JAK/STAT3, TNF-α via NF-κB, apoptosis, and oxidative phosphorylation, with related proteins such as IL2, IL6, and TNFα, which we subsequently submitted to experimental verification.

CONCLUSION

Innovatively stemming from from PCD interactions, in this study, we discerned PCDs with an independent impact on DN: apoptosis and NETotic cell death. We further screened DN evolution- and progression-related biomarkers, i.e. IL33, RPL11, and CX3CR1, all of which we empirically validated. This study not only poroposes a PCD-centric perspective for DN studies but also provides evidence for PCD-mediated immune cell infiltration exploration in DN regulation. Our results could motivate further exploration of DN pathogenesis, such as how the inflammatory microenvironment mediates NETotic cell death in DN regulation, representing a promising direction for future research.

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.

The Role of Angiotensin-Converting Enzyme (ACE) Polymorphisms in the Risk of Development and Treatment of Diabetic Nephropathy

BACKGROUND

Angiotensin-converting enzyme (ACE) is responsible for the production of angiotensin II, and increased production of angiotensin II is observed in diabetes. What is more, ACE polymorphisms may play a role in the development of diabetic nephropathy. The aim of this study was to assess the role of selected ACE polymorphisms (rs4343 and rs4646994) in the risk of development of diabetic nephropathy and in the likelihood of renal replacement therapy.

METHODS

ACE polymorphisms were analyzed in a group of 225 patients who were divided into three subgroups. The rs4343 polymorphism was determined using the PCR-RFLP, and the rs4646994 polymorphism was determined using the PCR. Molecular docking between domains of ACE and its ligands was performed by using AutoDock Vina.

RESULTS

The G/G genotype of rs4343 polymorphism is associated with increased odds of developing diabetic nephropathy. The G allele is also associated with a higher risk of this disease. Similar results were obtained in patients who had already had a kidney transplant as a result of diabetic nephropathy.

CONCLUSIONS

The presence of G/G and G/A genotypes, and the G allele increases the likelihood of developing diabetic nephropathy. This may also be a risk factor for renal replacement therapy.

Identification and Validation of Glomeruli Cellular Senescence-Related Genes in Diabetic Nephropathy by Multiomics

Accumulating evidence indicates that cellular premature senescence of the glomerulus, including endothelial cells, mesangial cells, and podocytes leads to diabetic nephropathy (DN), and DN is regarded as a clinical model of premature senescence. However, the role of cellular senescence-associated genes in the glomerulus in DN progression remains unclear. Therefore, this work aims to identify and validate potential cellular aging-related genes in the glomerulus in DN to provide novel clues for DN treatment based on anti-aging. The microarray GSE96804 dataset, including 41 diabetic glomeruli and 20 control glomeruli, is retrieved from the Gene Expression Omnibus (GEO) database and cellular senescence-related genes (CSRGs) are obtained from the GeneCards database and literature reports. Subsequently, PPI, GO, and KEGG enrichment are analyzed by screening the intersection between differentially expressed genes (DEGs) and CSRGs. scRNA-seq dataset GSE127235 is used to verify core genes expression in glomerulocytes of mice. Finally, db/db mice are utilized to validate the hub gene expression in the glomeruli, and high glucose-induced mesangial cells are used to confirm key gene expression. This study reveals that FOS and ZFP36 may play an anti-aging role in DN to ameliorate cell intracellular premature aging in mesangial cells of glomeruli.

The Link between Trace Metal Elements and Glucose Metabolism: Evidence from Zinc, Copper, Iron, and Manganese-Mediated Metabolic Regulation

Trace metal elements are of vital importance for fundamental biological processes. They function in various metabolic pathways after the long evolution of living organisms. Glucose is considered to be one of the main sources of biological energy that supports biological activities, and its metabolism is tightly regulated by trace metal elements such as iron, zinc, copper, and manganese. However, there is still a lack of understanding of the regulation of glucose metabolism by trace metal elements. In particular, the underlying mechanism of action remains to be elucidated. In this review, we summarize the current concepts and progress linking trace metal elements and glucose metabolism, particularly for the trace metal elements zinc, copper, manganese, and iron.

Potential protective effects of Huanglian Jiedu Decoction against COVID-19-associated acute kidney injury: A network-based pharmacological and molecular docking study

Corona virus disease 2019 (COVID-19) is prone to induce multiple organ damage. The kidney is one of the target organs of SARS-CoV-2, which is susceptible to inducing acute kidney injury (AKI). Huanglian Jiedu Decoction (HLJDD) is one of the recommended prescriptions for COVID-19 with severe complications. We used network pharmacology and molecular docking to explore the therapeutic and protective effects of HLJDD on COVID-19-associated AKI. Potential targets related to "HLJDD," "COVID-19," and "Acute Kidney Injury/Acute Renal Failure" were identified from several databases. A protein-protein interaction (PPI) network was constructed and screened the core targets according to the degree value. The target genes were then enriched using gene ontology and Kyoto Encyclopedia of Genes and Genomes. The bioactive components were docked with the core targets. A total of 65 active compounds, 85 common targets for diseases and drugs were obtained; PPI network analysis showed that the core protein mainly involved JUN, RELA, and AKT1; functional analysis showed that these target genes were mainly involved in lipid and atherosclerosis signaling pathway and IL-17 signal pathway. The results of molecular docking showed that JUN, RELA, and AKT1 had good binding activity with the effective chemical components of HLJDD. In conclusion, HLJDD can be used as a potential therapeutic drug for COVID-19-associated AKI.