Identifying Potential Diagnostic Genes for Diabetic Nephropathy Based on Hypoxia and Immune Status

Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics

Spatially resolved transcriptomics (SRT) studies are becoming increasingly common and large, offering unprecedented opportunities in mapping complex tissue structures and functions. Here we present integrative and reference-informed tissue segmentation (IRIS), a computational method designed to characterize tissue spatial organization in SRT studies through accurately and efficiently detecting spatial domains. IRIS uniquely leverages single-cell RNA sequencing data for reference-informed detection of biologically interpretable spatial domains, integrating multiple SRT slices while explicitly considering correlations both within and across slices. We demonstrate the advantages of IRIS through in-depth analysis of six SRT datasets encompassing diverse technologies, tissues, species and resolutions. In these applications, IRIS achieves substantial accuracy gains (39-1,083%) and speed improvements (4.6-666.0) in moderate-sized datasets, while representing the only method applicable for large datasets including Stereo-seq and 10x Xenium. As a result, IRIS reveals intricate brain structures, uncovers tumor microenvironment heterogeneity and detects structural changes in diabetes-affected testis, all with exceptional speed and accuracy.

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.

Investigation of shared genetic features and related mechanisms between diabetes and tuberculosis

OBJECTIVE

This study aimed to integrate bioinformatics technology to explore shared hub genes and related mechanisms between diabetes and tuberculosis and to provide a theoretical basis for revealing the disease mechanisms in patients with both diabetes and tuberculosis.

METHODS

Differentially expressed genes and Venn analysis were used to identify shared genes between diabetes and tuberculosis. PPI network analysis was used to screen key hub genes. GO and KEGG analyses were used to analyze the potential biological functions of these key hub genes. Immune infiltration analysis was performed using the ssGSEA algorithm. EnrichR online analysis website was used to explore potential therapeutic drugs.

RESULTS

The dataset analysis showed that PSMB9, ISG15, RTP4, CXCL10, GBP2, and GBP3 were six hub genes shared by diabetes and tuberculosis, which not only could distinguish between the two disease samples but also had a high diagnostic rate. GO and KEGG analyses showed that these six genes mainly mediate immune-related biological processes such as interferon, interleukin, and chemokine receptor binding, as well as signaling pathways such as RIG-I-like receptor, NOD-like receptor, and proteasome. Immune infiltration analysis showed that high expression of TIL may mediate the development of both diabetes and tuberculosis. In addition, suloctidil HL60 UP, thioridazine HL60 UP, mefloquine HL60 UP, 1-NITROPYRENE CTD 00001569, and chlorophyllin CTD 00000324 were the candidate drugs predicted by this study that were most likely to target hub genes.

CONCLUSION

Six differentially expressed genes shared by both diseases (PSMB9, ISG15, RTP4, CXCL10, GBP2, and GBP3) may play a key role in the disease progression of patients with both diabetes and tuberculosis. Candidate drugs targeting these hub genes have therapeutic potential and are worthy of further research. In summary, this study reveals potential shared pathogenic mechanisms between tuberculosis and diabetes.

Pathogenesis of diabetic complications: Exploring hypoxic niche formation and HIF-1α activation

Hypoxia is a common feature of diabetic tissues, which highly correlates to the progression of diabetes. The formation of hypoxic context is induced by disrupted oxygen homeostasis that is predominantly driven by vascular remodeling in diabetes. While different types of vascular impairments have been reported, the specific features and underlying mechanisms are yet to be fully understood. Under hypoxic condition, cells upregulate hypoxia-inducible factor-1α (HIF-1α), an oxygen sensor that coordinates oxygen concentration and cell metabolism under hypoxic conditions. However, diabetic context exploits this machinery for pathogenic functions. Although HIF-1α protects cells from diabetic insult in multiple tissues, it also jeopardizes cell function in the retina. To gain a deeper understanding of hypoxia in diabetic complications, we focus on the formation of tissue hypoxia and the outcomes of HIF-1α dysregulation under diabetic context. Hopefully, this review can provide a better understanding on hypoxia biology in diabetes.

Genes associated with inflammation for prognosis prediction for clear cell renal cell carcinoma: a multi-database analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the largest subtype of kidney tumour, with inflammatory responses characterising all stages of the tumour. Establishing the relationship between the genes related to inflammatory responses and ccRCC may help the diagnosis and treatment of patients with ccRCC.

Methods

First, we obtained the data for this study from a public database. After differential analysis and Cox regression analysis, we obtained the genes for the establishment of a prognostic model for ccRCC. As we used data from multiple databases, we standardized all the data using the surrogate variable analysis (SVA) package to make the data from different sources comparable. Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation. The data used for modelling and internal validation came from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) series (GSE29609) databases. ccRCC data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Tumour data from the E-MTAB-1980 cohort were used for external validation. The GSE40453 and GSE53757 datasets were used to verify the differential expression of inflammation-related gene model signatures (IRGMS). The immunohistochemistry of IRGMS was queried through the Human Protein Atlas (HPA) database. After the adequate validation of the IRGM, we further explored its application by constructing nomograms, pathway enrichment analysis, immunocorrelation analysis, drug susceptibility analysis, and subtype identification.

Results

The IRGM can robustly predict the prognosis of samples from patients with ccRCC from different databases. The verification results show that nomogram can accurately predict the survival rate of patients. Pathway enrichment analysis showed that patients in the high-risk (HR) group were associated with a variety of tumorigenesis biological processes. Immune-related analysis and drug susceptibility analysis suggested that patients with higher IRGM scores had more treatment options.

Conclusions

The IRGMS can effectively predict the prognosis of ccRCC. Patients with higher IRGM scores may be better candidates for treatment with immune checkpoint inhibitors and have more chemotherapy options.

Notoginsenoside R1 can inhibit the interaction between FGF1 and VEGFA to retard podocyte apoptosis

BACKGROUND

Diabetic nephropathy (DN) is a chronic condition resulting from microangiopathy in a high-glucose environment. The evaluation of vascular injury in DN has primarily focused on active molecules of VEGF, namely VEGFA and VEGF2(F2R). Notoginsenoside R1 (NGR1), a traditional anti-inflammatory medication, exhibits vascular activity. Therefore, identifying classical drugs with vascular inflammatory protection for the treatment of DN is a valuable pursuit.

METHODS

The "Limma" method was employed to analyze the glomerular transcriptome data, while the Spearman algorithm for Swiss target prediction was utilized to analyze the drug targets of NGR1. The molecular docking technique was employed to investigate the relationship between vascular active drug targets, and the COIP experiment was conducted to verify the interaction between fibroblast growth factor 1 (FGF1) and VEGFA in relation to NGR1 and drug targets.

RESULTS

According to the Swiss target prediction, the LEU32(b) site of the Vascular Endothelial Growth Factor A (VEGFA) protein, as well as the Lys112(a), SER116(a), and HIS102(b) sites of the Fibroblast Growth Factor 1 (FGF1) protein, are potential binding sites for NGR1 through hydrogen bonding. Additionally, the Co-immunoprecipitation (COIP) results suggest that VEGFA and FGF1 proteins can interact with each other, and NGR1 can impede this interaction. Furthermore, NGR1 can suppress the expression of VEGFA and FGF1 in a high-glucose environment, thereby decelerating podocyte apoptosis.

CONCLUSION

The inhibition of the interaction between FGF1 and VEGFA by NGR1 has been observed to decelerate podocyte apoptosis.

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.

Circ ASAP2 decreased inflammation and ferroptosis in diabetic nephropathy through SOX2/SLC7A11 by miR-770-5p

AIMS

Diabetes nephropathy (DN) is one of the major complications in diabetes. With the improvement of people's living standards in China in recent years, the incidence of diabetes has become the main cause of end-stage renal disease. However, how and whether circ ASAP2 could mediate DN remain poorly understood. This study aimed to determine the function and its biological mechanism of circ ASAP2 on inflammation and ferroptosis of DN.

METHODS

C57BL/6 mice were fed with a high-fat diet and injected with streptozotocin. Human renal glomerular endothelial cells stimulated with 20 mmol/L D-glucose.

RESULTS

In mice model DN, circular ASAP2 expression level was down-regulated, and miR-770-5p expression level was up-regulated. Moreover, the inhibition of ASAP2 aggravated diabetic nephropathy in mice model. The inhibition of ASAP2 promoted inflammation and oxidative stress to aggravate renal injury in mice model. Circular ASAP2 was reducing inflammation and oxidative stress in vitro model. The inhibition of ASAP2 promoted ferroptosis in model of DN. CASAP2 suppressed miR-770-5p in DN. Additionally, miR-770-5p aggravated diabetic nephropathy in mice model. MiR-770-5p promoted inflammation and oxidative stress to aggravate renal injury in mice model. MiR-770-5p was increasing inflammation and oxidative stress in vitro model. Circular ASAP2 induced SLC7A11 expression in model of DN through SOX2 by miR-770-5p.

CONCLUSIONS

These results suggest that circ ASAP2 decreased inflammation and ferroptosis in DN through SOX2/SLC7A11 by miR-770-5p, which might serve as a target for improving the role of ferroptosis in DN.

Identification of Markers for Diagnosis and Treatment of Diabetic Kidney Disease Based on the Ferroptosis and Immune

BACKGROUND

In advanced diabetic kidney disease (DKD), iron metabolism and immune dysregulation are abnormal, but the correlation is not clear. Therefore, we aim to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with immune inflammatory response and to identify new diagnostic biomarkers to help treat and diagnose DKD.

METHODS

Download data from gene expression omnibus (GEO) database and FerrDb database, and construct random forest tree (RF) and support vector machine (SVM) model to screen hub ferroptosis genes (DE-FRGs). We used consistent unsupervised consensus clustering to cluster DKD samples, and enrichment analysis was performed by Gene Set Variation Analysis (GSVA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) and then assessed immune cell infiltration abundance using the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms. Ferroptosis scoring system was established based on the Boruta algorithm, and then, core compounds were screened, and binding sites were predicted by Coremine Medical database.

RESULTS

We finally established a 7-gene signature (DUSP1, PRDX6, PEBP1, ZFP36, GABARAPL1, TSC22D3, and RGS4) that exhibited good stability across different datasets. Consistent clustering analysis divided the DKD samples into two ferroptosis modification patterns. Meanwhile, autophagy and peroxisome pathways and immune-related pathways can participate in the regulation of ferroptosis modification patterns. The abundance of immune cell infiltration differs significantly across patterns. Further, molecular docking results showed that the core compound could bind to the protein encoded by the core gene.

CONCLUSIONS

Our findings suggest that ferroptosis modification plays a crucial role in the diversity and complexity of the DKD immune microenvironment, and the ferroptosis score system can be used to effectively verify the relationship between ferroptosis and immune cell infiltration in DKD patients. Kaempferol and quercetin may be potential drugs to improve the immune and inflammatory mechanisms of DKD by affecting ferroptosis.

Hypoxia-related tumor environment correlated with immune infiltration and therapeutic sensitivity in diffuse large B-cell lymphoma

Background: Due to the high heterogeneity of diffuse large B-cell lymphoma (DLBCL), traditional chemotherapy treatment ultimately failed in one-third of the patients. Big challenges existed in finding how to accurately predict prognosis and provide individualized treatment. Hypoxia, although being a key factor in the development and progression of DLBCL, plays its role in DLBCL prognosis, which has yet to be fully explored.

Methods: Data used in the current study were sourced from the Gene Expression Omnibus (GEO) database. DLBCL patients were divided according to different hypoxia-related subtypes based on the expressions of hypoxia-related genes (HRGs) relevant to survival. Differentially expressed genes (DEGs) between subtypes were identified using the limma package. Using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses, the prognostic signature was established to calculate risk scores. The tumor microenvironment (TME) in low- and high-risk groups was evaluated by single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE. The chemotherapeutic sensitivity in two groups was assessed by IC50 values.

Results: DLBCL patients were clustered into two hypoxia-related subtype groups according to different gene survival and expressions associated with increasing oxygen delivery and reducing oxygen consumption, and these two subtype groups were compared. Based on the differential expression, a risk model was established using univariate cox and LASSO regression analyses, FNDC1, ANTXR1, RARRES2, S100A9, and MT1M. The performance of the risk signature in predicting the prognosis of DLBCL patients was validated in the internal and external datasets, as evidenced by receiver operating characteristic (ROC) curves. In addition, we observed significant differences in the tumor microenvironment and chemotherapeutic response between low- and high-risk groups.

Conclusion: Our study developed novel hypoxia-related subtypes in DLBCL and identified five prognostic signatures for DLBCL patients. These findings may enrich our understanding of the role of hypoxia in DLBCL and help improve the treatment of DLBCL patients.

Schisandrin A from Schisandra chinensis Attenuates Ferroptosis and NLRP3 Inflammasome-Mediated Pyroptosis in Diabetic Nephropathy through Mitochondrial Damage by AdipoR1 Ubiquitination

Schisandra chinensis, as a Chinese functional food, is rich in unsaturated fatty acids, minerals, vitamins, and proteins. Hence, this study was intended to elucidate the effects and biological mechanism of Schisandrin A from Schisandra chinensis in DN. C57BL/6 mice were fed with a high-fat diet and then injected with streptozotocin (STZ). Human renal glomerular endothelial cells were stimulated with 20 mmol/L d-glucose for DN model. Schisandrin A presented acute kidney injury in mice of DN. Schisandrin A reduced oxidative stress and inflammation in model of DN. Schisandrin A reduced high glucose-induced ferroptosis and reactive oxygen species (ROS-)-mediated pyroptosis by mitochondrial damage in model of DN. Schisandrin A directly targeted AdipoR1 protein and reduced LPS+ATP-induced AdipoR1 ubiquitination in vitro model. Schisandrin A activated AdipoR1/AMPK signaling pathway and suppressed TXNIP/NLRP3 signaling pathway in vivo and in vitro model of DN. Conclusively, our study revealed that Schisandrin A from Schisandra chinensis attenuates ferroptosis and NLRP3 inflammasome-mediated pyroptosis in DN by AdipoR1/AMPK-ROS/mitochondrial damage. Schisandrin A is a possible therapeutic option for DN or other diabetes.

Integrated Bioinformatics and Clinical Correlation Analysis of Key Genes, Pathways, and Potential Therapeutic Agents Related to Diabetic Nephropathy

Background

Diabetic nephropathy (DN) is a common microvascular complication of diabetes and a major cause of end-stage renal disease, resulting in a substantial socioeconomic burden around the world. Some unknown biomarkers, mechanisms, and potential novel agents regarding DN are yet to be identified.

Methods

GSE30528 and GSE1009 were downloaded as training datasets to identify differentially expressed genes (DEGs) of DN. Common DEGs were selected for further analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs were performed to explore molecular mechanisms and pathways. Protein-protein interaction (PPI) network of DEGs was used to identify the top 10 hub genes of DN. Expression profiles of the hub genes were validated in GSE96804 and GSE47183 datasets. The clinical correlation analyses were conducted to confirm the association between key genes and clinical characteristics in the Nephroseq v5 database. The Drug Gene Interaction Database was used to predict potential targeted drugs.

Results

345 and 1228 DEGs were identified in GSE30528 and GSE1009, respectively; and 120 common DEGs were found. The biological process of DEGs was significantly enriched in kidney development. PI3K-Akt signaling pathway, focal adhesion, complement and coagulation cascades were significantly enriched KEGG pathways. The identified top10 hub genes were VEGFA, NPHS1, WT1, TJP1, CTGF, FYN, SYNPO, PODXL, TNNT2, and BMP2. VEGFA, NPHS1, WT1, CTGF, SYNPO, PODXL, and TNNT2 were significantly downregulated in DN. VEGFA, NPHS1, WT1, CTGF, SYNPO, and PODXL were positively correlated with glomerular filtration rate. The targeted drugs or molecular compounds were enalapril, sildenafil, and fenofibrate target for VEGFA; losartan target for NPHS1; halofuginone, deferoxamine, curcumin, and sirolimus target for WT1; and purpurogallin target for TNNT2.

Conclusions

VEGFA, NPHS1, WT1, CTGF, SYNPO, and PODXL are promising biomarkers for diagnosing and evaluating the progression of DN. The drug-gene interaction analyses provide a list of candidate drugs for the precise treatment of DN.

Upregulated Proteasome Subunits in COVID-19 Patients: A Link with Hypoxemia, Lymphopenia and Inflammation

Severe COVID-19 disease leads to hypoxemia, inflammation and lymphopenia. Viral infection induces cellular stress and causes the activation of the innate immune response. The ubiquitin-proteasome system (UPS) is highly implicated in viral immune response regulation. The main function of the proteasome is protein degradation in its active form, which recognises and binds to ubiquitylated proteins. Some proteasome subunits have been reported to be upregulated under hypoxic and hyperinflammatory conditions. Here, we conducted a prospective cohort study of COVID-19 patients (n = 44) and age-and sex-matched controls (n = 20). In this study, we suggested that hypoxia could induce the overexpression of certain genes encoding for subunits from the α and β core of the 20S proteasome and from regulatory particles (19S and 11S) in COVID-19 patients. Furthermore, the gene expression of proteasome subunits was associated with lymphocyte count reduction and positively correlated with inflammatory molecular and clinical markers. Given the importance of the proteasome in maintaining cellular homeostasis, including the regulation of the apoptotic and pyroptotic pathways, these results provide a potential link between COVID-19 complications and proteasome gene expression.