Mitochondrial RNAs as Potential Biomarkers of Functional Impairment in Diabetic Kidney Disease.
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ABSTRACT:
SUBMITTER: Di Mauro S
PROVIDER: S-EPMC9331991 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Diabetic kidney disease (DKD) is the most common diabetic complication and is a leading cause of end-stage kidney disease. Increasing evidence shows that DKD is regulated not only by many classical signaling pathways but also by epigenetic mechanisms involving chromatin histone modifications, DNA methylation, and non-coding RNA (ncRNAs). In this review, we focus on our current understanding of the role and mechanisms of ncRNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in the pathogenesis of DKD. Of them, the regulatory role of TGF-β/Smad3-dependent miRNAs and lncRNAs in DKD is highlighted. Importantly, miRNAs and lncRNAs as biomarkers and therapeutic targets for DKD are also described, and the perspective of ncRNAs as a novel therapeutic approach for combating diabetic nephropathy is also discussed.
| S-EPMC7870688 | biostudies-literature
Project description:Diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in diabetes. The quest for both prognostic and surrogate endpoint biomarkers for advanced DKD and end-stage renal disease has received major investment and interest in recent years. However, at present no novel biomarkers are in routine use in the clinic or in trials. This review focuses on the current status of prognostic biomarkers. First, we emphasise that albuminuria and eGFR, with other routine clinical data, show at least modest prediction of future renal status if properly used. Indeed, a major limitation of many current biomarker studies is that they do not properly evaluate the marginal increase in prediction on top of these routinely available clinical data. Second, we emphasise that many of the candidate biomarkers for which there are numerous sporadic reports in the literature are tightly correlated with each other. Despite this, few studies have attempted to evaluate a wide range of biomarkers simultaneously to define the most useful among these correlated biomarkers. We also review the potential of high-dimensional panels of lipids, metabolites and proteins to advance the field, and point to some of the analytical and post-analytical challenges of taking initial studies using these and candidate approaches through to actual clinical biomarker use.
| S-EPMC6448994 | biostudies-literature
Project description:Polycystic kidney disease is characterized by the progressive development of kidney cysts and declining renal function with frequent development of cysts in other organs including the liver. The polycystic kidney (PCK) rat is a rodent model of polycystic liver disease that has been used to study hepatorenal disease progression and evaluate pharmacotherapeutic interventions. Biomarkers that describe the cyst progression, liver impairment, and/or hepatic cyst burden could provide clinical utility for this disease. In the present study, hepatic cyst volume was measured by magnetic resonance imaging in PCK rats at 12, 16, and 20 weeks. After 20 weeks, Sprague Dawley (n = 4) and PCK (n = 4) rats were sacrificed and 42 bile acids were analyzed in the liver, bile, serum, and urine by liquid chromatography coupled to tandem mass spectrometry. Bile acid profiling revealed significant increases in total bile acids (molar sum of all measured bile acids) in the liver (13-fold), serum (6-fold), and urine (3-fold) in PCK rats, including those speciated bile acids usually associated with hepatotoxicity. Total serum bile acids correlated with markers of liver impairment (liver weight, total liver bile acids, total hepatotoxic liver bile acids, and cyst volume [ r > 0.75; P < 0.05]). Based on these data, serum bile acids may be useful biomarkers of liver impairment in polycystic hepatorenal disease.
| S-EPMC5877312 | biostudies-literature
Project description:BackgroundAs one of the most prevalent diagnostic indicators of diabetic kidney disease (DKD), albumin-to-creatinine ratio (ACR) shows considerably limited predictive power in clinical application. We analyzed microarray expression profiling of urine to seek for differentially expressed miRNAs for potential biomarkers of DKD.MethodsUrine samples from type 2 diabetes mellitus (T2DM) patients with (30 mg/g < ACR < 300 mg/g, DKD group) or without DKD (ACR < 30 mg/g, DM group) were collected for miRNA microarray analysis. The differentially expressed miRNAs were screened by bioinformatics analysis and validated by quantitative real-time PCR. Target genes of differentially expressed miRNAs were predicted in miRDB, Targetscan, and microRNA.org databases. We also conducted the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways analysis to explore for potential mechanisms in DKD.ResultsNine miRNAs were down-regulated and seventeen miRNAs were up-regulated in DKD group, compared to DM group. The levels of miR-3137 and miR-4270 in DKD group were 0.670 ± 0.505 and 2.116 ± 1.762 times than those in DM group, respectively, showing great significance. A total of 1076 target genes were simultaneously predicted by miRDB, Targetscan, and microRNA.org databases. According to the GO analysis results, disorders of endomembrane system may be one of the major pathological changes in DKD. In addition, Rap 1 signaling pathway is also altered obviously in DKD, discovered by the KEGG analysis.ConclusionMiR-3137 and miR-4270 show the potential for urinary biomarkers of DKD. The pathological changes of DKD may be related to disorders of endomembrane system and alternation of Rap1 signaling pathway.
| S-EPMC7755759 | biostudies-literature
Project description:BackgroundThere are big differences in treatments and prognosis between diabetic kidney disease (DKD) and non-diabetic renal disease (NDRD). However, DKD patients couldn't be diagnosed early due to lack of special biomarkers. Urine is an ideal non-invasive sample for screening DKD biomarkers. This study aims to explore DKD special biomarkers by urinary proteomics.Materials and methodsAccording to the result of renal biopsy, 142 type 2 diabetes mellitus (T2DM) patients were divided into 2 groups: DKD (n = 83) and NDRD (n = 59). Ten patients were selected from each group to define urinary protein profiles by label-free quantitative proteomics. The candidate proteins were further verifyied by parallel reaction monitoring (PRM) methods (n = 40). Proteins which perform the same trend both in PRM and proteomics were verified by enzyme-linked immunosorbent assays (ELISA) with expanding the sample size (n = 82). The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of diagnostic biomarkers.ResultsWe identified 417 peptides in urinary proteins showing significant difference between DKD and NDRD. PRM verification identified C7, SERPINA4, IGHG1, SEMG2, PGLS, GGT1, CDH2, CDH1 was consistent with the proteomic results and p < 0.05. Three potential biomarkers for DKD, C7, SERPINA4, and gGT1, were verified by ELISA. The combinatied SERPINA4/Ucr and gGT1/Ucr (AUC = 0.758, p = 0.001) displayed higher diagnostic efficiency than C7/Ucr (AUC = 0.632, p = 0.048), SERPINA4/Ucr (AUC = 0.661, p = 0.032), and gGT1/Ucr (AUC = 0.661, p = 0.029) respectively.ConclusionsThe combined index SERPINA4/Ucr and gGT1/Ucr can be considered as candidate biomarkers for diabetic nephropathy after adjusting by urine creatinine.
| S-EPMC10873681 | biostudies-literature
Project description:Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility. Result:A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis. Conclusion:Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.
| S-EPMC7072115 | biostudies-literature
Project description:Not much is known about the roles of long non-coding RNAs (lncRNAs) for chronic kidney disease (CKD). In this study, we included CKD patient cohorts and normal controls as a discovery cohort to identify putative lncRNA biomarkers associated with CKD. We first compared the lncRNA expression profiles of CKD patients with normal controls, and identified differentially expressed lncRNAs and mRNAs. Co-expression network based on the enriched differentially expressed mRNAs and lncRNAs was constructed using WGCNA to identify important modules related to CKD. A lncRNA-miRNA-mRNA pathway network based on the hub lncRNAs and mRNAs, related miRNAs, and overlapping pathways was further constructed to reveal putative biomarkers. A total of 821 significantly differentially expressed mRNAs and lncRNAs were screened between CKD and control samples, which were enriched in nine modules using weighted correlation network analysis (WGCNA), especially brown and yellow modules. Co-expression network based on the enriched differentially expressed mRNAs and lncRNAs in brown and yellow modules uncovered 7 hub lncRNAs and 53 hub mRNAs. A lncRNA-miRNA-mRNA pathway network further revealed that lncRNAs of HCP5 and NOP14-AS1 and genes of CCND2, COL3A1, COL4A1, and RAC2 were significantly correlated with CKD. The lncRNAs of NOP14-AS1 and HCP5 were potential prognostic biomarkers for predicting the risk of CKD.
| S-EPMC6853076 | biostudies-literature
Project description:Diabetic kidney disease (DKD) is one of the major microvascular complications of diabetes mellitus, with relatively high morbidity and mortality globally but still in short therapeutic options. Over the decades, a large body of data has demonstrated that oxidative stress, inflammatory responses, and hemodynamic disorders might exert critical influence in the initiation and development of DKD, whereas the delicate pathogenesis of DKD remains profoundly elusive. Recently, long non-coding RNAs (lncRNAs), extensively studied in the field of cancer, are attracting increasing attentions on the development of diabetes mellitus and its complications including DKD, diabetic retinopathy, and diabetic cardiomyopathy. In this review, we chiefly focused on abnormal expression and function of lncRNAs in major resident cells (mesangial cell, endothelial cell, podocyte, and tubular epithelial cell) in the kidney, summarized the critical roles of lncRNAs in the pathogenesis of DKD, and elaborated their potential therapeutic significance, in order to advance our knowledge in this field, which might help in future research and clinical treatment for the disease.
| S-EPMC9065414 | biostudies-literature
Project description:In patients with diabetes mellitus, poor metabolic control has a long-lasting impact on kidney disease development. Epigenetic changes, including cytosine methylation, have been proposed as potential mediators of the long-lasting effect of adverse metabolic events. Our understanding of the presence and contribution of methylation changes to disease development is limited because of the lack of comprehensive base-resolution methylome information of human kidney tissue samples and site-specific methylation editing. Base resolution, whole-genome bisulfite sequencing methylome maps of human diabetic kidney disease (DKD) tubule samples, and associated gene expression measured by RNA sequencing highlighted widespread methylation changes in DKD. Pathway analysis highlighted coordinated (methylation and gene expression) changes in immune signaling, including tumor necrosis factor alpha (TNF). Changes in TNF methylation correlated with kidney function decline. dCas9-Tet1-based lowering of the cytosine methylation level of the TNF differentially methylated region resulted in an increase in the TNF transcript level, indicating that methylation of this locus plays an important role in controlling TNF expression. Increasing the TNF level in diabetic mice increased disease severity, such as albuminuria. In summary, our results indicate widespread methylation differences in DKD kidneys and highlights epigenetic changes in the TNF locus and its contribution to the development of nephropathy in patients with diabetes mellitus.
| S-EPMC6629092 | biostudies-literature
Project description:BackgroundDiabetic kidney disease (DKD) is a major complication of diabetes and the leading cause of end-stage renal disease worldwide. Renal inflammation and infiltration of immune cells contribute to the development and progression of DKD. Thus, the aim of the present study was to identify and validate immune-related biomarkers and analyze potential regulators including transcription factors (TFs), microRNAs (miRNAs), and drugs for DKD.MethodsImmune-related genes from the ImmPort database and glomeruli samples from GSE1009 and GSE30528 were used to identify differentially expressed immune-related genes (DEIRGs) of DKD. The expression level and clinical correlation analyses of DEIRGs were verified in the Nephroseq database. Murine podocytes were cultured to construct the high glucose-induced podocyte injury model. The reliability of the bioinformatics analysis was experimentally validated by RT-qPCR in podocytes. Networks among DEIRGs, regulators, and drugs were constructed to predict potential regulatory mechanisms for DKD.ResultsDKD-associated DEIRGs were identified. CCL19 and IL7R were significantly upregulated in the DKD group and negatively correlated with glomerular filtration rate (GFR). GHR, FGF1, FYN, VEGFA, F2R, TGFBR3, PTGDS, FGF9, and SEMA5A were significantly decreased in the DKD group and positively correlated with GFR. RT-qPCR showed that the relative mRNA expression levels of GHR, FGF1, FYN, TGFBR3, PTGDS, FGF9, and SEMA5A were significantly down-regulated in the high glucose-induced podocyte injury group. The enriched regulators for DEIRGs included 110 miRNAs and 8 TFs. The abnormal expression of DEIRGs could be regulated by 16 established drugs.ConclusionsThis study identified immune-related biomarkers, regulators, and drugs of DKD. The findings of the present study provide novel insights into immune-related diagnosis and treatment of DKD.
| S-EPMC10150481 | biostudies-literature