Bioinformatics Analysis Reveals Crosstalk Among Platelets, Immune Cells, and the Glomerulus That May Play an Important Role in the Development of Diabetic Nephropathy

How Protein Depletion Balances Thrombosis and Bleeding Risk in the Context of Platelet's Activatory and Negative Signaling

Platelets are small cell fragments that play a crucial role in hemostasis, requiring fast response times and fine signaling pathway regulation. For this regulation, platelets require a balance between two pathway types: the activatory and negative signaling pathways. Activatory signaling mediators are positive responses that enhance stimuli initiated by a receptor in the platelet membrane. Negative signaling regulates and controls the responses downstream of the same receptors to roll back or even avoid spontaneous thrombotic events. Several blood-related pathologies can be observed when these processes are unregulated, such as massive bleeding in activatory signaling inhibition or thrombotic events for negative signaling inhibition. The study of each protein and metabolite in isolation does not help to understand the role of the protein or how it can be contrasted; however, understanding the balance between active and negative signaling could help develop effective therapies to prevent thrombotic events and bleeding disorders.

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.

Whole-transcriptome defines novel glucose metabolic subtypes in colorectal cancer

Colorectal cancer (CRC) is the most prevalent malignancy of the digestive system. Glucose metabolism plays a crucial role in CRC development. However, the heterogeneity of glucose metabolic patterns in CRC is not well characterized. Here, we classified CRC into specific glucose metabolic subtypes and identified the key regulators. 2228 carbohydrate metabolism-related genes were screened out from the GeneCards database, 202 of them were identified as prognosis genes in the TCGA database. Based on the expression patterns of the 202 genes, three metabolic subtypes were obtained by the non-negative matrix factorization clustering method. The C1 subtype had the worst survival outcome and was characterized with higher immune cell infiltration and more activation in extracellular matrix pathways than the other two subtypes. The C2 subtype was the most prevalent in CRC and was characterized by low immune cell infiltration. The C3 subtype had the smallest number of individuals and had a better prognosis, with higher levels of NRF2 and TP53 pathway expression. Secreted frizzled-related protein 2 (SFRP2) and thrombospondin-2 (THBS2) were confirmed as biomarkers for the C1 subtype. Their expression levels were elevated in high glucose condition, while their knockdown inhibited migration and invasion of HCT 116 cells. The analysis of therapeutic potential found that the C1 subtype was more sensitive to immune and PI3K-Akt pathway inhibitors than the other subtypes. To sum up, this study revealed a novel glucose-related CRC subtype, characterized by SFRP2 and THBS2, with poor prognosis but possible therapeutic benefits from immune and targeted therapies.

Multiple-microarray analysis for identification of key genes involved in diabetic nephropathy

The purpose of our study was to discover genes with significantly aberrant expression in diabetic nephropathy (DN) and to determine their potential mechanism. We acquired renal tubules, glomerulus and blood samples data from DN patients and controls from the GEO database. The differentially expressed genes (DEGs) in renal tubules, glomerulus and blood samples between DN patients and controls were studied. Based on these DEGs, we carried out the functional annotation and constructed protein-protein interaction (PPI) network. By comparing DN patients and controls of DEGs, we acquired the shared DGEs in renal tubules, glomerulus and blood samples of DN patients and controls. DN patients compared to controls, we obtained 3000 DEGs, 3064 DEGs, and 2296 DEGs in renal tubules, glomerulus and blood samples, respectively. The PPI networks of top 40 DEGs in renal tubules, glomerulus and blood samples was consisted of 229 nodes and 229 edges, 540 nodes and 606 edges, and 132 nodes and 124 edges, respectively. In total, 21 shared genes were finally found, including CASP3, DHCR24, CXCL1, GYPC, INHBA, LTF, MT1G, MUC1, NINJ1, PFKFB3, PPP1R3C, CCL5, SRSF7, PHLDA2, RBM39, WTAP, BASP1, PLK2, PDK2, PNPLA4, and SNED1. These genes may be associated with the DN process. Our study provides a basis to explore the potential mechanism and identify novel therapeutic targets for DN.

The role of cellular crosstalk in the progression of diabetic nephropathy

Diabetic nephropathy (DN) is one of the most common complications of diabetes, and its main manifestations are progressive proteinuria and abnormal renal function, which eventually develops end stage renal disease (ESRD). The pathogenesis of DN is complex and involves many signaling pathways and molecules, including metabolic disorders, genetic factors, oxidative stress, inflammation, and microcirculatory abnormalities strategies. With the development of medical experimental techniques, such as single-cell transcriptome sequencing and single-cell proteomics, the pathological alterations caused by kidney cell interactions have attracted more and more attention. Here, we reviewed the characteristics and related mechanisms of crosstalk among kidney cells podocytes, endothelial cells, mesangial cells, pericytes, and immune cells during the development and progression of DN and highlighted its potential therapeutic effects.

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

BACKGROUND

Systemic inflammation and immune response are involved in the pathogenesis of diabetic nephropathy (DN). However, the specific immune-associated signature during DN development is unclear. Our study aimed to reveal the roles of immune-related genes during DN progression.

METHODS

The GSE30529 and GSE30528 datasets were acquired from the Gene Expression Omnibus (GEO) database. Then, the intersection between differentially expressed genes (DEGs) and immune score-related genes (ISRGs) was screened. Subsequently, functional enrichment analyses were performed. The different immune phenotype-related subgroups were finally divided using unsupervised clustering. The core genes were identified by WGCNA and the protein-protein interaction (PPI) network. xCell algorithm was applied to assess the proportion of immune cell infiltration.

RESULTS

92 immune score-related DEGs (ISRDEGs) were identified, and these genes were enriched in inflammation- and immune-associated pathways. Furthermore, two distinct immune-associated subgroups (C1 and C2) were identified, and the C1 subgroup exhibited activated immune pathways and a higher percentage of immune cells compared to the C2 subgroup. Two core genes (LCK and HCK) were identified and all up-regulated in DN, and the expressions were verified using GSE30122, GSE142025, and GSE104954 datasets. GSEA indicated the core genes were mainly enriched in immune-related pathways. Correlation analysis indicated LCK and HCK expressions were positively correlated with aDC, CD4+ Tem, CD8+T cells, CD8+ Tem, and mast cells.

CONCLUSIONS

We identified two immune-related genes and two immune-associated subgroups, which might help to design more precise tailored immunotherapy for DN patients.