A review of renal GH/IGF1 family gene expression in chronic kidney diseases

IGF1 and PPARG polymorphisms are associated with reduced estimated glomerular filtration rate in a cohort of children and adolescents with type 1 diabetes

INTRODUCTION

Several genetic loci have been associated with diabetic nephropathy; however, the underlying genetic mechanisms are still poorly understood, with no robust candidate genes identified yet.

AIM

We aimed to determine whether two polymorphisms, previously associated with renal decline, influence kidney impairment evaluating their association with markers of renal function in a pediatric population with type 1 diabetes (T1D).

MATERIAL AND METHODS

Renal function was evaluated by glomerular filtration rate (eGFR) and albumin-to-creatinine ratio (ACR) in a cohort of pediatric subjects with T1D (n = 278). Risk factors for diabetes complications (diabetes duration, blood pressure, HbA1c) were assessed. The IGF1 rs35767 and PPARG rs1801282 SNPs were genotyped by TaqMan RT-PCR system. An additive genetic interaction was calculated. Association analysis between markers of renal function and both SNPs or their additive interaction were performed.

RESULTS

Both SNPs showed a significant association with eGFR: the A allele of rs35767 or the C allele of rs1801282 were associated to reduced eGFR compared to G alleles. Multivariate regression analysis adjusted for age, sex, z-BMI, T1D duration, blood pressure and Hba1c values showed that the additive genetic interaction was independently associated with lower eGFR (β = -3.59 [-6.52 to -0.66], p = 0.017). No associations were detected between SNPs, their additive interaction and ACR.

CONCLUSIONS

These results provide new insight into the genetic predisposition to renal dysfunction, showing that two polymorphisms in IGF1 and PPARG genes can lead to a reduction in renal filtration rate leading these patients to be exposed to a higher risk of early renal complications.

Integrated multiple-microarray analysis and mendelian randomization to identify novel targets involved in diabetic nephropathy

Background

Diabetic nephropathy (DN), which is the main cause of renal failure in end-stage renal disease, is becoming a common chronic renal disease worldwide. Mendelian randomization (MR) is a genetic tool that is widely used to minimize confounding and reverse causation when identifying the causal effects of complex traits. In this study, we conducted an integrated multiple microarray analysis and large-scale plasma proteome MR analysis to identify candidate biomarkers and evaluate the causal effects of prospective therapeutic targets in DN.

Methods

Five DN gene expression datasets were selected from the Gene Expression Omnibus. The robust rank aggregation (RRA) method was used to integrate differentially expressed genes (DEGs) of glomerular samples between patients with DN and controls, followed by functional enrichment analysis. Protein quantitative trait loci were incorporated from seven different proteomic genome-wide association studies, and genetic association data on DN were obtained from FinnGen (3676 cases and 283,456 controls) for two-sample MR analysis. External validation and clinical correlation were also conducted.

Results

A total of 82 DEGs (53 upregulated and 29 downregulated) were identified through RRA integrated analysis. The enriched Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathways of the DEGs were significantly enriched in neutrophil degranulation, neutrophil activation, proteoglycan binding, collagen binding, secretory granule lumen, gluconeogenesis, tricarboxylic acid cycle, and pentose phosphate pathways. MR analysis revealed that the genetically predicted levels of MHC class I polypeptide-related sequence B (MICB), granzyme A (GZMA), cathepsin S (CTSS), chloride intracellular channel protein 5, and ficolin-1 (FCN1) were causally associated with DN risk. Expression validation and clinical correlation analysis showed that MICB, GZMA, FCN1, and insulin-like growth factor 1 may participate in the development of DN, and carbonic anhydrase 2 and lipoprotein lipase may play protective roles in patients with DN.

Conclusion

Our integrated analysis identified novel biomarkers, including MICB and GZMA, which may help further understand the complicated mechanisms of DN and identify new target pathways for intervention.

A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery

The kidney's cellular diversity is on par with its physiological intricacy; yet identifying cell populations and their markers remains challenging. Here, we created a comprehensive atlas of the healthy adult mouse kidney (MKA: Mouse Kidney Atlas) by integrating 140.000 cells and nuclei from 59 publicly available single-cell and single-nuclei RNA-sequencing datasets from eight independent studies. To harmonize annotations across datasets, we built a hierarchical model of the cell populations. Our model allows the incorporation of novel cell populations and the refinement of known profiles as more datasets become available. Using MKA and the learned model of cellular hierarchies, we predicted previously missing cell annotations from several studies. The MKA allowed us to identify reproducible markers across studies for poorly understood cell types and transitional states, which we verified using existing data from micro-dissected samples and spatial transcriptomics.

Identification and validation of immune-related biomarkers and potential regulators and therapeutic targets for diabetic kidney disease

BACKGROUND

Diabetic 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.

METHODS

Immune-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.

RESULTS

DKD-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.

CONCLUSIONS

This 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.

Increased activity of the metalloproteinase PAPP-A promotes diabetes-induced glomerular hypertrophy

BACKGROUND

Diabetic nephropathy (DN) is a serious complication of diabetes and a common cause of end stage renal failure. Insulin-like growth factor (IGF)-signaling has been implicated in DN, but is mechanistically poorly understood. Here, we assessed the activity of the metalloproteinase PAPP-A, an activator of IGF activity, and its possible interaction with the endogenous PAPP-A inhibitors stanniocalcin (STC)-1 and -2 in the mammalian kidney under normal and hyperglycemic conditions.

METHODS AND RESULTS

Immunohistochemistry demonstrated that PAPP-A, its proteolytic substrate IGF binding protein-4, STC1 and STC2 are present in the human kidney. Endogenous inhibited complexes of PAPP-A (PAPP-A:STC1 and PAPP-A:STC2) were demonstrated in media conditioned by human mesangial cells (HMCs), suggesting that PAPP-A activity is regulated by the STCs in kidney tissue. A method for the selective detection of active PAPP-A in tissue was developed and a significant increase in glomerular active PAPP-A in human diabetic kidney relative to normal was observed. In DN patients, the estimated glomerular filtration rate correlated with PAPP-A activity. In diabetic mice, glomerular growth was reduced when PAPP-A activity was antagonized by adeno-associated virus-mediated overexpression of STC2.

CONCLUSION

We propose that PAPP-A activity in renal tissue is precisely balanced by STC1 and STC2. An imbalance in this equilibrium causing increased PAPP-A enzymatic activity potentially contributes to the development of DN, and thus, therapeutic targeting of PAPP-A activity may represent a novel strategy for its treatment.