Advances in Clinical Research in Chronic Kidney Disease

PAR2 activation on human tubular epithelial cells engages converging signaling pathways to induce an inflammatory and fibrotic milieu

Key features of chronic kidney disease (CKD) include tubulointerstitial inflammation and fibrosis. Protease activated receptor-2 (PAR2), a G-protein coupled receptor (GPCR) expressed by the kidney proximal tubular cells, induces potent proinflammatory responses in these cells. The hypothesis tested here was that PAR2 signalling can contribute to both inflammation and fibrosis in the kidney by transactivating known disease associated pathways. Using a primary cell culture model of human kidney tubular epithelial cells (HTEC), PAR2 activation induced a concentration dependent, PAR2 antagonist sensitive, secretion of TNF, CSF2, MMP-9, PAI-1 and CTGF. Transcription factors activated by the PAR2 agonist 2F, including NFκB, AP1 and Smad2, were critical for production of these cytokines. A TGF-β receptor-1 (TGF-βRI) kinase inhibitor, SB431542, and an EGFR kinase inhibitor, AG1478, ameliorated 2F induced secretion of TNF, CSF2, MMP-9, and PAI-1. Whilst an EGFR blocking antibody, cetuximab, blocked PAR2 induced EGFR and ERK phosphorylation, a TGF-βRII blocking antibody failed to influence PAR2 induced secretion of PAI-1. Notably simultaneous activation of TGF-βRII (TGF-β1) and PAR2 (2F) synergistically enhanced secretion of TNF (2.2-fold), CSF2 (4.4-fold), MMP-9 (15-fold), and PAI-1 (2.5-fold). In summary PAR2 activates critical inflammatory and fibrotic signalling pathways in human kidney tubular epithelial cells. Biased antagonists of PAR2 should be explored as a potential therapy for CKD.

Combined Serologic and Genetic Risk Score and Prognostication of Phospholipase A2 receptor-Associated Membranous Nephropathy

INTRODUCTION

The aim of this study was to test whether a combined risk score on the basis of genetic risk and serology can improve the prediction of kidney failure in phospholipase A2 receptor (PLA2R)-associated primary membranous nephropathy.

METHODS

We performed a retrospective analysis of 519 biopsy-proven PLA2R-associated primary membranous nephropathy patients with baseline eGFR ≥25 ml/min per 1.73 m 2 . The combined risk score was calculated by combining the genetic risk score with PLA2R ELISA antibody titers. The primary end point was kidney disease progression defined as a 50% reduction in eGFR or kidney failure. Cox proportional hazard regression analysis and C-statistics were applied to compare the performance of PLA2R antibody, genetic risk score, and combined risk score, as compared with clinical factors alone, in predicting primary outcomes.

RESULTS

The median age was 56 years (range, 15-82 years); the male-to-female ratio was 1:0.6, the median eGFR at biopsy was 99 ml/min per 1.73 m 2 (range: 26-167 ml/min per 1.73 m 2 ), and the median proteinuria was 5.3 g/24 hours (range: 1.5-25.8 g/24 hours). During a median follow-up of 67 (5-200) months, 66 (13%) had kidney disease progression. In Cox proportional hazard regression models, PLA2R antibody titers, genetic risk score, and combined risk score were all individually associated with kidney disease progression with and without adjustments for age, sex, proteinuria, eGFR, and tubulointerstitial lesions. The best-performing clinical model to predict kidney disease progression included age, eGFR, proteinuria, serum albumin, diabetes, and tubulointerstitial lesions (C-statistic 0.76 [0.69-0.82], adjusted R 2 0.51). Although the addition of PLA2R antibody titer improved the performance of this model (C-statistic: 0.78 [0.72-0.84], adjusted R 2 0.61), replacing PLA2R antibody with the combined risk score improved the model further (C-statistic: 0.82 [0.77-0.87], adjusted R 2 0.69, difference of C-statistics with clinical model=0.06 [0.03-0.10], P < 0.001; difference of C-statistics with clinical-serologic model=0.04 [0.01-0.06], P < 0.001).

CONCLUSIONS

In patients with PLA2R-associated membranous nephropathy, the combined risk score incorporating inherited risk alleles and PLA2R antibody enhanced the prediction of kidney disease progression compared with PLA2R serology and clinical factors alone.

Targeting DNA methylation in diabetic kidney disease: A new perspective

Diabetic kidney disease (DKD) is a leading diabetic complication causing significant mortality among people around the globe. People with poor glycemic control accompanied by hyperinsulinemia, dyslipidemia, hypertension, and obesity develop diabetic complications. These diabetic patients develop epigenetic changes and suffer from diabetic kidney complications even after subsequent glucose control, the phenomenon that is recognized as metabolic memory. DNA methylation is an essential epigenetic modification that contributes to the development and progression of several diabetic complications, including DKD. The aberrant DNA methylation pattern at CpGs sites within several genes, such as mTOR, RPTOR, IRS2, GRK5, SLC27A3, LCAT, and SLC1A5, associated with the accompanying risk factors exacerbate the DKD progression. Although drugs such as azacytidine and decitabine have been approved to target DNA methylation for diseases such as hematological malignancies, none have been approved for the treatment of DKD. More importantly, no DNA hypomethylation-targeting drugs have been approved for any disease conditions. Understanding the alteration in DNA methylation and its association with the disease risk factors is essential to target DKD effectively. This review has discussed the abnormal DNA methylation pattern and the kidney tissue-specific expression of critical genes involved in DKD onset and progression. Moreover, we also discuss the new possible therapeutic approach that can be exploited for treating DNA methylation aberrancy in a site-specific manner against DKD.

Navigating the Crossroads: Understanding the Link Between Chronic Kidney Disease and Cardiovascular Health

Chronic Kidney Disease (CKD) has emerged as a global healthcare challenge affecting a significant portion of the world's population. This comprehensive narrative review delves into the intricate relationship between CKD and cardiovascular disease (CVD). CKD is characterized by kidney damage persisting for at least three months, often with or without a decline in glomerular filtration rate (GFR). It is closely linked with CVD, as individuals with CKD face a high risk of cardiovascular events, making cardiovascular-associated mortality a significant concern in advanced CKD stages. The review emphasizes the importance of precise risk assessment using biomarkers, advanced imaging, and tailored medication strategies to mitigate cardiovascular risks in CKD patients. Lifestyle modifications, early intervention, and patient-centered care are crucial in managing both conditions. Challenges in awareness and recognition of CKD and the need for comprehensive interdisciplinary care are highlighted. Recent advances in research offer promising therapies, such as SGLT2 inhibitors, MRAs, GLP-1R agonists, and selective endothelin receptor antagonists. Stem cell-based therapies, gene editing, and regenerative approaches are under investigation. Patient-physician "risk discussions" and tailored risk assessments are essential for improving patient outcomes. In conclusion, the review underscores the complexity of the interconnected CKD and cardiovascular health domains. Ongoing research, innovative therapies, and personalized healthcare will be instrumental in addressing the challenges, reducing the disease burden, and enhancing well-being for individuals facing CKD and cardiovascular issues. Recognizing the intricate connections between these conditions is imperative for healthcare providers, policymakers, and researchers as they seek to improve the quality of care and outcomes for affected individuals.

A Comprehensive Weighted Gene Co-expression Network Analysis Uncovers Potential Targets in Diabetic Kidney Disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman's correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

A comprehensive weighted gene co-expression network analysis uncovers potential targets in diabetic kidney disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman’s correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

Dapagliflozin Ameliorates Renal Tubular Ferroptosis in Diabetes via SLC40A1 Stabilization

Tubular injury has been shown to play a critical role in the morbidity of diabetic kidney disease (DKD); ferroptosis often occurs in tubules during renal disease development. This study was aimed at evaluating the inhibitory effects and potential mechanism of dapagliflozin (DAPA) against diabetic-related ferroptosis in the kidney. C57BL/6 mice were fed a high-fat diet (HFD) for 12 weeks, administered a small dose of streptozocin (STZ) for three consecutive days by intraperitoneal injection, and then orally administered dapagliflozin (10 mg/kg/day) for 8 weeks. Mouse blood and urine samples were collected, and their renal cortices were harvested for subsequent investigations. The effects of DAPA were also evaluated in HK-2 cells subjected to simulated diabetic conditions through excess glucose or palmitic acid (PA) administration. DAPA significantly ameliorated tubular injury independently of glycemic control in diabetic model mice. In vivo and in vitro investigations showed that dapagliflozin ameliorated tubular injury by inhibiting ferroptosis. Docking experiments demonstrated that dapagliflozin and SLC40A1 could bind with each other and may consequently reduce ubiquitination degradation. In conclusion, in this study, the tubular protective effects of DAPA, irrespective of glycemic control, were observed in a diabetic mouse model. DAPA ameliorated ferroptosis during diabetic tubular injury via SLC40A1 stabilization, and this may be the mechanism underlying its action. To the best of our knowledge, this is the first study to investigate the ferroptosis inhibitory effects of DAPA in the treatment of DKD.

Smartphone-Based Quantitative Analysis of Protein Array Signals for Biomarker Detection in Lupus

Fluorescence-based microarray offers great potential in clinical diagnostics due to its high-throughput capability, multiplex capabilities, and requirement for a minimal volume of precious clinical samples. However, the technique relies on expensive and complex imaging systems for the analysis of signals. In the present study, we developed a smartphone-based application to analyze signals from protein microarrays to quantify disease biomarkers. The application adopted Android Studio open platform for its wide access to smartphones, and Python was used to design a graphical user interface with fast data processing. The application provides multiple user functions such as "Read", "Analyze", "Calculate" and "Report". For rapid and accurate results, we used ImageJ, Otsu thresholding, and local thresholding to quantify the fluorescent intensity of spots on the microarray. To verify the efficacy of the application, three antigens each with over 110 fluorescent spots were tested. Particularly, a positive correlation of over 0.97 was achieved when using this analytical tool compared to a standard test for detecting a potential biomarker in lupus nephritis. Collectively, this smartphone application tool shows promise for cheap, efficient, and portable on-site detection in point-of-care diagnostics.