Discovery of a Biomarker Signature That Reveals a Molecular Mechanism Underlying Diabetic Kidney Disease via Organ Cross Talk.
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ABSTRACT:
SUBMITTER: Wu IW
PROVIDER: S-EPMC9210869 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features give an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features give an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.
2022-10-12 | GSE215221 | GEO
Project description:Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.
| S-EPMC9630270 | biostudies-literature
Project description:Discovery of a Trans-Omics Biomarker Signature that Predisposes High Risk Diabetic Patients to Diabetic Kidney Disease
| PRJNA889394 | ENA
Project description:A protein biomarker discovery workflow was applied to plasma samples from patients at different stages of diabetic kidney disease. The proteomics platform produced a panel of significant plasma biomarkers that were statistically scrutinised against the current gold standard tests on an analysis of 572 patients. Five proteins were significantly associated with diabetic kidney disease defined by albuminuria, renal impairment (eGFR) and chronic kidney disease staging (CKD Stage ≥1, ROC curve of 0.77). The results prove the suitability and efficacy of the process used, and introduce a biomarker panel with the potential to improve diagnosis of diabetic kidney disease.
| S-EPMC5988498 | biostudies-literature
Project description:Investigation of the transcriptional and chromatin-accessibility effects of Eed knockout in the developing mouse stomach and intestinal mesenchyme using RNA-seq and ATAC-seq.
2022-12-19 | GSE147418 | GEO
Project description:BackgroundDiabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the world. Emerging evidence has shown that urinary mRNAs may serve as early diagnostic and prognostic biomarkers of DKD. In this article, we aimed to first establish a novel bioinformatics-based methodology for analyzing the "urinary kidney-specific mRNAs" and verify their potential clinical utility in DKD.MethodsTo select candidate mRNAs, a total of 127 Affymetrix microarray datasets of diabetic kidney tissues and other tissues from humans were compiled and analyzed using an integrative bioinformatics approach. Then, the urinary expression of candidate mRNAs in stage 1 study (n?=?82) was verified, and the one with best performance moved on to stage 2 study (n?=?80) for validation. To avoid potential detection bias, a one-step Taqman PCR assay was developed for quantification of the interested mRNA in stage 2 study. Lastly, the in situ expression of the selected mRNA was further confirmed using fluorescent in situ hybridization (FISH) assay and bioinformatics analysis.ResultsOur bioinformatics analysis identified sixteen mRNAs as candidates, of which urinary BBOX1 (uBBOX1) levels were significantly upregulated in the urine of patients with DKD. The expression of uBBOX1 was also increased in normoalbuminuric diabetes subjects, while remained unchanged in patients with urinary tract infection or bladder cancer. Besides, uBBOX1 levels correlated with glycemic control, albuminuria and urinary tubular injury marker levels. Similar results were obtained in stage 2 study. FISH assay further demonstrated that BBOX1 mRNA was predominantly located in renal tubular epithelial cells, while its expression in podocytes and urothelium was weak. Further bioinformatics analysis also suggested that tubular BBOX1 mRNA expression was quite stable in various types of kidney diseases.ConclusionsOur study provided a novel methodology to identify and analyze urinary kidney-specific mRNAs. uBBOX1 might serve as a promising biomarker of DKD. The performance of the selected urinary mRNAs in monitoring disease progression needs further validation.
| S-EPMC6394064 | biostudies-literature
Project description:The initiation of de novo testis cord organization in the fetal gonad is poorly understood. Endothelial cell migration into XY gonads initiates testis morphogenesis. However, neither the signals that regulate vascularization of the gonad nor the mechanisms through which vessels affect tissue morphogenesis are known. Here, we show that Vegf signaling is required for gonad vascularization and cord morphogenesis. We establish that interstitial cells express Vegfa and respond, by proliferation, to endothelial migration. In the absence of vasculature, four-dimensional imaging of whole organs revealed that interstitial proliferation is reduced and prevents formation of wedge-like structures that partition the gonad into cord-forming domains. Antagonizing vessel maturation also reduced proliferation. However, proliferation of mesenchymal cells was rescued by the addition of PDGF-BB. These results suggest a pathway that integrates initiation of vascular development and testis cord morphogenesis, and lead to a model in which undifferentiated mesenchyme recruits blood vessels, proliferates in response, and performs a primary function in the morphogenesis and patterning of the developing organ.
| S-EPMC3017142 | biostudies-literature
Project description:Fibrinolysis and pericellular proteolysis depend on molecular coassembly of plasminogen and its activator on cell, fibrin, or matrix surfaces. We report here the existence of a fibrinolytic cross-talk mechanism bypassing the requirement for their molecular coassembly on the same surface. First, we demonstrate that, despite impaired binding of Glu-plasminogen to the cell membrane by epsilon-aminocaproic acid (epsilon-ACA) or by a lysine-binding site-specific mAb, plasmin is unexpectedly formed by cell-associated urokinase (uPA). Second, we show that Glu-plasminogen bound to carboxy-terminal lysine residues in platelets, fibrin, or extracellular matrix components (fibronectin, laminin) is transformed into plasmin by uPA expressed on monocytes or endothelial cell-derived microparticles but not by tissue-type plasminogen activator (tPA) expressed on neurons. A 2-fold increase in plasmin formation was observed over activation on the same surface. Altogether, these data indicate that cellular uPA but not tPA expressed by distinct cells is specifically involved in the recognition of conformational changes and activation of Glu-plasminogen bound to other biologic surfaces via a lysine-dependent mechanism. This uPA-driven cross-talk mechanism generates plasmin in situ with a high efficiency, thus highlighting its potential physiologic relevance in fibrinolysis and matrix proteolysis induced by inflammatory cells or cell-derived microparticles.
| S-EPMC2896557 | biostudies-literature
Project description:ObjectiveUrinary proteomics is primarily applied to the study of renal and urogenital tract disorders. Here are reported two distinct successful examples of this approach for the discovery of early urinary biomarkers of kidney-related dysfunctions: diabetic nephropathy (DN), a well-known complication of diabetes frequently leading to dialysis, and drug-induced nephrotoxicity, a possible condition caused by medication-overuse headache (MOH). Early detection of kidney disorders based on selective biomarkers could permit to diagnose patients at the initial stage of the disease, where the therapy may be suspended or prevent disease advancement.MethodsUrine samples were first concentrated and desalted. Subsequently, they were subjected to two-dimensional gel electrophoresis (2-DE) coupled to mass spectrometry (MS) for protein identification. Furthermore, some proteins were verified by Western blot and ELISA test.ResultsIn diabetes-related study, 11 differentially expressed proteins were detected (8 up-regulated and 3 down-regulated) in type 2 diabetic (T2D) and T2DN patients compared to the healthy control subjects. In the MOH study, a total of 21 over-excreted proteins were revealed in urine of non-steroidal anti-inflammatory drugs (NSAIDs) and mixtures abusers vs controls. Particularly, 4 proteins were positively validated by immunob-lotting and EUSA.ConclusionUrinary proteomics allows non-invasive assessment of renal diseases at an early stage by the identification of characteristic protein pattern.
| S-EPMC6295592 | biostudies-literature
Project description:Diabetic kidney disease is the leading cause of ESRD, but few biomarkers of diabetic kidney disease are available. This study used gas chromatography-mass spectrometry to quantify 94 urine metabolites in screening and validation cohorts of patients with diabetes mellitus (DM) and CKD(DM+CKD), in patients with DM without CKD (DM-CKD), and in healthy controls. Compared with levels in healthy controls, 13 metabolites were significantly reduced in the DM+CKD cohorts (P?0.001), and 12 of the 13 remained significant when compared with the DM-CKD cohort. Many of the differentially expressed metabolites were water-soluble organic anions. Notably, organic anion transporter-1 (OAT1) knockout mice expressed a similar pattern of reduced levels of urinary organic acids, and human kidney tissue from patients with diabetic nephropathy demonstrated lower gene expression of OAT1 and OAT3. Analysis of bioinformatics data indicated that 12 of the 13 differentially expressed metabolites are linked to mitochondrial metabolism and suggested global suppression of mitochondrial activity in diabetic kidney disease. Supporting this analysis, human diabetic kidney sections expressed less mitochondrial protein, urine exosomes from patients with diabetes and CKD had less mitochondrial DNA, and kidney tissues from patients with diabetic kidney disease had lower gene expression of PGC1? (a master regulator of mitochondrial biogenesis). We conclude that urine metabolomics is a reliable source for biomarkers of diabetic complications, and our data suggest that renal organic ion transport and mitochondrial function are dysregulated in diabetic kidney disease.
| S-EPMC3810086 | biostudies-other