Diabetic kidney disease (DKD) affects an estimated 20–40% of type 2 diabetes patients and is among the most prevalent microvascular complications in this patient population, contributing to high morbidity and mortality rates. Currently, changes in albuminuria status are thought to be a primary indicator of the onset or progression of DKD, yet progressive nephropathy and renal impairment can occur in certain diabetic individuals who exhibit normal urinary albumin levels, emphasizing the lack of sensitivity and specificity associated with the use of albuminuria as a biomarker for detecting diabetic kidney disease and predicting DKD risk. According to the study, a non-invasive method for early detection or prediction of DKD may involve combining proteomic analytical techniques such second generation sequencing, mass spectrometry, two-dimensional gel electrophoresis, and other advanced system biology algorithms. Another category of proteins of relevance may now be provided by renal tissue biomarkers. The establishment of reliable proteomic biomarkers of DKD represents a novel approach to improving the diagnosis, prognostic evaluation, and treatment of affected patients. In the present review, a series of protein biomarkers that have been characterized to date are discussed, offering a theoretical foundation for future efforts to aid patients suffering from this debilitating microvascular complication.

Dynamic RNPs (ribonucleoprotein particles) are formed when RNAbinding proteins assemble with RNA. RNP compositions vary depending on the maturation or the functional state of the RNA as well as the cell environment. [1][2][3] All aspects of RNA life, including splicing, transcription, intracellular trafficking, modification, translation, as well as decay, are regulated by RBPs. In turn, RNA can modulate the location or activity of RBPs through a process called "riboregulation." 4 The PRK (protein kinase R) induces autophosphorylation and dimerization of proteins through their binding to double-stranded RNA, which activates the enzyme and is a prime example of riboregulation (Figure 1). 5 RBP dysfunction and dysregulation are correlated to several muscular atrophies and neurological disorder diseases in humans, like amyotrophic lateral sclerosis, 6 cancer, 7 and genetic abnormalities. 8 RBPs are conserved evolutionarily and have a wide distribution in tissues, in line with their common roles concerning housekeeping. In spite of these attributes, alterations or mutations in RBPs responsible for housekeeping can often lead to specific tissue defects.How does this occur? First, RBPs possibly act on their RNA targets or regulatory partners that express tissue specificity. Secondly, RBPs may attach to RNA targets with various specificities and affinities, regulated by modifications in RNA post-translation, their interactions, and local structure or sequence, causing regulatory complex formation to specific cell types. 9-12 Third, attachment of RNA by itself may not always lead to regulatory effects. Even though RBPs are capable of binding hundreds of RNA targets, only some of them are regulated in particular cellular conditions. In RNA regulons, a set of RNAs are coordinated and regulated by a given RBP under the influence of stimuli. 13,14 Finally, the RBPs cause the formation of extensive network structures with their RNA targets and other

Diabetic microangiopathy is among the most common complications affecting patients with diabetes, and includes both diabetic retinopathy (DR) and diabetic nephropathy (DKD). Diabetic microangiopathy remains a persistent threat to the health and quality of life of affected patients. Mechanistically, the severity of DR and DKD is tied to mitochondrial and glucose metabolism abnormalities, with the activation of the glycolytic enzyme pyruvate kinase M2 (PKM2) contributing to mitochondrial and glomerular dysfunction, abnormal renal hemodynamics, and retinopathy. PKM2 can activate inflammatory bodies in macrophages to promote the release of inflammatory mediators, and serves as a key regulator of inflammatory factors, chemokines and adhesion molecules. As such, there is sufficient evidence that PKM2 can be used as a biomarker for the diagnosis of diabetes and diabetic microangiopathy. Here, we survey the mechanisms whereby PKM2 contributes to diabetes-related microvascular diseases, associated regulatory roles, post-translational modifications, and the potential utility of PKM2 as a therapeutic target. Through this literature review, we have determined that PKM2 offers promise as both a diagnostic marker and therapeutic target with direct relevance to research pertaining to diabetic microangiopathy.

Introduction: Diabetic nephropathy (DN) is related to type 1 and type 2 diabetes. They are the leading cause of end-stage renal disease (ESRD), but the underling specific pathogenesis of DN is not yet clear. Our study was conducted to explore how DN changed the transcriptome profiles in kidney. Methods: The gene expression profile of micro-dissected glomeruli of 41 type 2 diabetic nephropathy patients and 20 healthy controls were included. The sample data set GSE86804 was obtained from the GEO database. Differentially expressed genes (DEGs) were analyzed in R with the limma package and the important modules were found by weighted gene co-expression network analysis (WGCNA) clustering. The modules were then analyzed based on Gene Ontology (GO) gene set enrichment analysis, and the hub genes were found out. We next validated the hub gene, PDK4, in a cell model of DN. We also constructed the PDK4-related PPI network to investigate the correlation between PDK4 expression and other genes. Results: Heat map and volcano map were drawn to illustrate the mRNA expression profile of 1204 DEGs in both samples of diabetic nephropathy patients and the control group. Using WGCNA, we selected the blue module in which genes showed the strongest correlation with the phenotype and the smallest P-value. We also identified PDK4 as a hub gene. PDK4 expression was upregulated in human diabetic kidney tissue. Moreover, PDK4 was speculated to play a role in glomerular basement membrane development, kidney development according to the enrichment of functions and signaling pathways. Furthermore, PDK4, and two key genes GSTA2 and G6PC protein expression were verified highly expressed in the cell model of DN. Conclusion: During the pathogenesis of DN, many genes may change expression in a coordinated manner. The discovery of PDK4 as key gene using WGCNA is of great significance for the development of new treatment strategies to block the development of DN.

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