A cis-eQTL in PFKFB2 is associated with diabetic nephropathy, adiposity and insulin secretion in American Indians

Epidemiological trends in diabetic renal complications in United States adults: A center for disease control and prevention wide-ranging online data for epidemiologic research analysis (1999-2020)

BACKGROUND

Renal complications of diabetes mellitus pose a significant public health challenge, contributing to substantial morbidity and mortality globally. Understanding temporal trends and regional disparities in mortality related to diabetic nephropathy is crucial for guiding targeted interventions and policy decisions.

AIM

To display the trends and disparities of diabetic nephropathy related mortality.

METHODS

A retrospective analysis was conducted using death certificate data from the center for disease control and prevention (CDC) wide-ranging online data for epidemiologic research analysis (WONDER) database, spanning from 1999 to 2020, to investigate mortality related to renal complications of diabetes in adults aged 35 or above. Age-adjusted mortality rate (AAMR) per 100000 persons and annual percent change (APC) were computed, with stratification by year, sex, race/ethnicity, and geographic region.

RESULTS

Between 1999 and 2020, a total of 525804 deaths occurred among adults aged 35 to 85+ years due to renal-related issues associated with diabetes. AAMR for renal-related deaths in adult diabetic patients showed a consistent increase from 1.6 in 1999 to 34.9 in 2020 (average APC [AAPC]: 17.23; 95% confidence interval [CI]: 13.35-28.79). Throughout the study period, men consistently had higher AAMR (overall AAMR for men: 17.8; 95%CI: 17.7-17.9). In 1999, the AAMR for men was 1.8, increasing to 44.2 by 2020 (AAPC: 17.54; 95%CI: 13.09-29.53), while for women, it was 1.6 in 1999 and rose to 27.6 by 2020 (AAPC: 15.55; 95%CI: 13.35-21.10). American Indian/Alaska Native adults exhibited the highest overall AAMR (36.1; 95%CI: 35.2-36.9), followed by Black/African American (25.5; 95%CI: 25.3-25.7). The highest mortality was observed in the Western (AAMR: 16.6; 95%CI: 16.5-16.7), followed by the Midwestern region (AAMR: 14.4; 95%CI: 14.314.4). Significant variations in AAMR were observed among different states, with Oklahoma recording the highest (21.2) and Connecticut the lowest (7). The CDC WONDER database could potentially have omissions or inaccuracies. It does not provide data outside of the available variables. Furthermore, dataset after 2020 was not included in this study.

CONCLUSION

Our findings highlight an alarming rise in mortality related to renal complications of diabetes among United States adults over the past two decades, with concerning disparities across demographic and geographic factors. These results underscore the urgent need for targeted interventions, policies, and protocols to address the growing burden of diabetic nephropathy and substantially reduce mortality rates in the United States. This will help improve the overall health outcome in the United States by identifying communities at risk and implementing tailored assistance to them.

scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis

The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.

Novel Molecular Networks and Regulatory MicroRNAs in Type 2 Diabetes Mellitus: Multiomics Integration and Interactomics Study

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions.

OBJECTIVE

The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis.

METHODS

Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM.

RESULTS

We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance.

CONCLUSIONS

This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways.

Subproteomic profiling from renal cortices in OLETF rats reveals mutations of multiple novel genes in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN) is a serious threat to human health, but its pathogenesis is not fully understood. Otsuka Long-Evans Tokushima Fatty (OLETF) rats are very similar to human DN in many aspects such as pathological changes and processes, and are deemed to be an ideal rodent model.

OBJECTIVE

This study was aimed to explore the pathogenesis of DN by analyzing the protein expression profile from renal cortices in OLETF rats.

METHODS

Thirty-six-week-old diabetic OLETF rats and normal control Long-Evans Tokushima Otsuka (LETO) rats were nephrectomized, and the renal cortices were isolated. The proteins were separated by soluble and insoluble high-resolution subproteomics methods for the analysis and identification of differential proteins.

RESULTS

Thirty-six differentially expressed proteins were found. Among them, 11 proteins had different isoelectric points and molecular weights between OLETF and LETO rats. Further sequencing identified point mutations in genes encoding eight of these proteins, which are involved in many biological processes closely related to DN, including oxidative stress and inflammation. Five of these eight proteins have not been reported in DN.

CONCLUSION

This study reveals mutations of multiple novel genes in diabetic OLETF rats, providing some new potential targets for the pathogenesis of DN and helping to better understand the pathogenesis of DN.

LncRNA XIST serves as a ceRNA to regulate the expression of ASF1A, BRWD1M, and PFKFB2 in kidney transplant acute kidney injury via sponging hsa-miR-212-3p and hsa-miR-122-5p

We aimed to identify potential mechanism associated with acute kidney injury (AKI) after kidney transplantation. The dataset GSE53771, which contained 18 zero-hour (ZERO group) and 18 selected post-transplant (POST group) biopsy samples from 18 kidney allografts (8 AKI and 10 controls) was downloaded from GEO database. Differentially expressed miRNAs (DEMIs) were screened using limma package, and bidirectional hierarchical clustering of the DEMIs was performed using the pheatmap package. Target genes of DEMIs were predicted by miRWalk 2.0, miRNA-target genes networks were presented using Cytoscape, protein-protein interaction (PPI) networks were constructed by STRING (version:10.0) database, and competing endogenous RNAs (ceRNA) regulating network were constructed using Cytoscape. In ZERO and POST groups, a total of 4 and 24 differentially expressed miRNAs were obtained in AKI samples compared with control, respectively. Specifically, 71 lncRNAs were obtained to interact with five miRNAs (hsa-miR-215-5p, hsa-miR-192-5p, hsa-miR-422a, hsa-miR-212-3p and hsa-miR-122-5p). Histone chaperone ASF1A (ASF1A) and bromodomain and WD repeat-containing protein 1(BRWD1) were targeted by hsa-miR-212-3p in PPI network. In ceRNA network, lncRNA XIST could interact with four miRNAs (hsa-miR-212-3p, hsa-miR-122-5p, hsa-miR-215-5p, and hsa-miR-192-5p). LncRNA XIST might serve as a ceRNA to sponge hsa-miR-212-3p to regulate the development of AKI via altering the expression of ASF1A/BRWD1. Furthermore, lncRNA XIST could also interact with hsa-miR-122-5p to modulate the expression of PFKFB2 in thyroid hormone signaling pathway and AMPK signaling pathway. LncRNA XIST can serve as a ceRNA to sponge hsa-miR-212-3p and hsa-miR-122-5p to regulate AKI progression via modulating the expression of ASF1A, BRWD1, and PFKFB2.[Figure: see text].

Increased expression and phosphorylation of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase isoforms in urinary exosomes in pre-eclampsia

Background

Glycolysis is altered in various kidney diseases, but little is known about glycolysis in pre-eclampsia, a multi-system disorder with major pathological effects on the kidney. Urinary exosomes provide a non-invasive alternative for studying changes in kidney metabolism. This study aims to characterise the expression and phosphorylation of isozymes of the key glycolytic regulatory protein, 6-phosphofructokinase-2-kinase/fructose-2,6-bisphosphatase (PFK-2/FBPase-2), in urinary exosomes of subjects with pre-eclampsia (PE), compared to normotensive non-pregnant (NC) and normotensive pregnant (NP) controls.

Methods

A cross-sectional study of NC (n = 19), NP (n = 23) and PE (n = 29) subjects was performed. Exosomes were isolated from urine samples by differential ultracentrifugation, and then analyzed by Western blot and densitometry for expression of PFK-2/FBPase-2 isozymes (PFKFB2, PFKFB3 and PFKFB4) and phosphorylation of PFKFB2 at residues Ser483 and Ser466 and PFKFB3 at Ser461.

Results

PFKFB2 expression was increased 4.7-fold in PE compared to NP (p < 0.001). PFKFB2 phosphorylation at Ser483 was increased 2.6-fold in PE compared to NP (p = 0.002). Expression of phosphorylated PFKFB2/PFKFB3 at Ser466/Ser461 was increased in PE, being present in 77.4% (95% CI 59.9–88.9%) of PE and 8.3% (95% CI 1.2–27.0%) of NP samples (p < 0.001). PFKFB3 was more commonly expressed in PE, detected in 90.3% (95% CI 74.3–97.4%) of PE and 8.3% (95% CI 1.2–27.0%) of NP samples (p < 0.001). PFKFB4 had a 7.2-fold increase in expression in PE compared to NP (p < 0.001). No significant differences between NP and NC groups were observed.

Conclusion

Regulatory proteins that increase glycolysis are increased in the urinary exosomes of subjects with pre-eclampsia, suggesting that renal glycolysis may be increased in this condition.

Diabetes relief in mice by glucose-sensing insulin-secreting human α-cells

Cell identity switches, where terminally-differentiated cells convert into different cell-types when stressed, represent a widespread regenerative strategy in animals, yet they are poorly documented in mammals. In mice, some glucagon-producing pancreatic α-cells and somatostatin-producing δ-cells become insulin expressers upon ablation of insulin-secreting β-cells, promoting diabetes recovery. Whether human islets also display this plasticity, especially in diabetic conditions, remains unknown. Here we show that islet non-β-cells, namely α-cells and PPY-producing γ–cells, obtained from deceased non-diabetic or diabetic human donors, can be lineage-traced and reprogrammed by the transcription factors Pdx1 and MafA to produce and secrete insulin in response to glucose. When transplanted into diabetic mice, converted human α-cells reverse diabetes and remain producing insulin even after 6 months. Surprisingly, insulin-producing α-cells maintain α-cell markers, as seen by deep transcriptomic and proteomic characterization. These observations provide conceptual evidence and a molecular framework for a mechanistic understanding of in situ cell plasticity as a treatment for diabetes and other degenerative diseases.

An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome

Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian "Deterministic Approximation of Posteriors" (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR < 0.05. 14% and 19% of GLOM and TI eQTLs, respectively, had >1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, "nephQTL."

Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

Blood glucose levels are tightly controlled by the coordinated action of at least four cell types constituting pancreatic islets. Changes in the proportion and/or function of these cells are associated with genetic and molecular pathophysiology of monogenic, type 1, and type 2 (T2D) diabetes. Cellular heterogeneity impedes precise understanding of the molecular components of each islet cell type that govern islet (dys)function, particularly the less abundant delta and gamma/pancreatic polypeptide (PP) cells. Here, we report single-cell transcriptomes for 638 cells from nondiabetic (ND) and T2D human islet samples. Analyses of ND single-cell transcriptomes identified distinct alpha, beta, delta, and PP/gamma cell-type signatures. Genes linked to rare and common forms of islet dysfunction and diabetes were expressed in the delta and PP/gamma cell types. Moreover, this study revealed that delta cells specifically express receptors that receive and coordinate systemic cues from the leptin, ghrelin, and dopamine signaling pathways implicating them as integrators of central and peripheral metabolic signals into the pancreatic islet. Finally, single-cell transcriptome profiling revealed genes differentially regulated between T2D and ND alpha, beta, and delta cells that were undetectable in paired whole islet analyses. This study thus identifies fundamental cell-type–specific features of pancreatic islet (dys)function and provides a critical resource for comprehensive understanding of islet biology and diabetes pathogenesis.

The impact of genetic variants on BMI increase during childhood versus adulthood

BACKGROUND

Genetic variants that predispose individuals to obesity may have differing influences during childhood versus adulthood, and additive effects of such variants are likely to occur. Our ongoing studies to identify genetic determinants of obesity in American Indians have identified 67 single-nucleotide polymorphisms (SNPs) that reproducibly associate with maximum lifetime non-diabetic body mass index (BMI). This study aimed to identify when, during the lifetime, these variants have their greatest impact on BMI increase.

SUBJECTS/METHODS

A total of 5906 Native Americans of predominantly Pima Indian heritage with repeated measures of BMI between the ages of 5 and 45 years were included in this study. The association between each SNP with the rates of BMI increase during childhood (5-19 years) and adulthood (20-45 years) were assessed separately. The significant SNPs were used to calculate a cumulative allelic risk score (ARS) for childhood and adulthood, respectively, to assess the additive effect of these variants within each period of life.

RESULTS

The majority of these SNPs (36 of 67) were associated with rate of BMI increase during childhood (P-value range: 0.00004-0.05), whereas only nine SNPs were associated with rate of BMI change during adulthood (P-value range: 0.002-0.02). These 36 SNPs associated with childhood BMI gain likely had a cumulative effect as a higher childhood-ARS associated with rate of BMI change (β=0.032 kg m(-2) per year per risk allele, 95% confidence interval: 0.027-0.036, P<0.0001), such that at age 19 years, individuals with the highest number of risk alleles had a BMI of 10.2 kg m(-2) greater than subjects with the lowest number of risk alleles.

CONCLUSIONS

Overall, our data indicates that genetic polymorphisms associated with lifetime BMI may influence the rate of BMI increase during different periods in the life course. The majority of these polymorphisms have a larger impact on BMI during childhood, providing further evidence that prevention of obesity will need to begin early in life.

Integrative Biology of Diabetic Kidney Disease

BACKGROUND

The leading cause of ESRD in the U.S. is diabetic kidney disease (DKD). Despite significant efforts to improve outcomes in DKD, the impact on disease progression has been disappointing. This has prompted clinicians and researchers to search for alternative approaches to identify persons at risk, and to search for more effective therapies to halt progression of DKD. Identification of novel therapies is critically dependent on a more comprehensive understanding of the pathophysiology of DKD, specifically at the molecular level. A more expansive and exploratory view of DKD is needed to complement more traditional research approaches that have focused on single molecules.

SUMMARY

In recent years, sophisticated research methodologies have emerged within systems biology that should allow for a more comprehensive disease definition of DKD. Systems biology provides an inter-disciplinary approach to describe complex interactions within biological systems including how these interactions influence systems' functions and behaviors. Computational modeling of large, system-wide, quantitative data sets is used to generate molecular interaction pathways, such as metabolic and cell signaling networks.

KEY MESSAGES

Importantly, interpretation of data generated by systems biology tools requires integration with enhanced clinical research data and validation using model systems. Such an integrative biological approach has already generated novel insights into pathways and molecules involved in DKD. In this review, we highlight recent examples of how combining systems biology with traditional clinical and model research efforts results in an integrative biology approach that has significantly added to the understanding of the complex pathophysiology of DKD.