In This Issue of Diabetes
By Max Bingham, PhD
WASH Regulates Glucose Homeostasis In Islets: Mouse In Vivo and Cell Data
A protein called Wiskott-Aldrich syndrome protein and SCAR homolog, or WASH, appears to have a role in glucose uptake and insulin release and specifically seems to be involved in the trafficking of the membrane glucose transporter Glut2. According to Ding et al. (p. 377), this could mean that patients with mutations in components of the WASH complex could be at risk of developing type 2 diabetes. The findings come from a series of experiments involving a mouse model with a pancreas-specific deletion of WASH. After confirming that WASH is expressed in pancreatic tissue sections, and particularly so in islets, the authors also confirmed that mice with the pancreas-specific WASH deletion had substantial reductions in levels of the WASH protein in pancreas samples. Although deletion of WASH did not affect pancreas development or body weight, it did result in impaired glucose clearance, which might (in part) be explained by defective insulin release in the model. Additionally, WASH deletion appears to decrease overall levels of the glucose transporter protein Glut2 and likely alters glucose-stimulated insulin secretion via reduced plasma membrane localization of the protein. Moving to experiments using the INS-1 cell line, the authors show that WASH is likely to have a role in glucose homeostasis, controlling endosome-to-membrane trafficking of Glut2. To confirm the role, they show that in the absence of WASH, Glut2 is trafficked to lysosomes and degraded, suggesting that WASH is critical for appropriate trafficking of the Glut2. Author Daniel D. Billadeau told Diabetes: “The data highlight not only a critical role for WASH in regulating glucose sensing in β-cells but also implicate this endosomal sorting pathway and the proteins that regulate receptor trafficking as potential modulators of insulin secretion by β-cells. Whether mutations or polymorphisms in genes encoding proteins that facilitate the endosomal recycling of glucose transporters in β-cells contribute to the development of type 2 diabetes will require further experimentation.”
WASH is necessary for glucose-stimulated insulin secretion in INS-1 cells through trafficking of Glut2. Immunofluorescence staining of insulin (green) and WASH (red) from small interfering control (siCtrl) and siWASH was analyzed. Scale bars, 20 µm.
Novel Peptides Identified to Tackle Fibrosis and Diabetic Nephropathy: Preclinical Data
Retarding renal fibrosis in diabetic nephropathy might be possible with an approach that targets cell division auto antigen 1 (CDA1), according to Chai et al. (p. 395). Working with a novel protein called CDA1BP1 (CDA1 binding protein 1) as well as a series of other peptides, the authors were able to reverse a range of molecular and pathological changes associated with diabetic nephropathy. In addition, they also show on a genetic level how the protein can attenuate the renal expression of genes involved in fibrotic and proinflammatory pathways. Using both genetic and pharmacological approaches, the authors initially identify the novel CDA1BP1 as a component that regulates the profibrotic nature of CDA1. Previously, they identified CDA1 as critical in promoting the activity of the key profibrotic growth factor, transforming growth factor-β (TGF-β). Using both cellular approaches and mouse models of diabetes, they show how CDA1BP1 is involved in regulating various fibrotic processes and then identify a peptide that can dose-dependently reduce the expression of various collagens involved in fibrosis (at least in cells). Finally, using a slightly altered version of the peptide, they found that in mice it could significantly attenuate renal expression of genes involved in fibrosis and reverse diabetes-associated renal changes such as extracellular matrix accumulation and glomerular injury. Author Zhonglin Chai commented: “A possible strategy to combat diabetic nephropathy is to reduce the pathologically enhanced TGF-β signaling to a relatively low level in order to inhibit its pathological action. Our studies show that targeting a novel TGF-β enhancer, the CDA1/CDA1BP1 axis, represents such an strategy. Indeed, our data demonstrate that our prototype peptide inhibitor of the CDA/CDA1BP1 axis can reduce but not block TGF-β signaling and retard experimental diabetic nephropathy in mice. This provides an opportunity to develop a new category of therapeutic agents to treat fibrotic diseases associated with enhanced TGF-β action, such as diabetic nephropathy.”
Genetic deletion of CDA1BP1 retards diabetic nephropathy in mice. Nondiabetic (Con) and 20-week diabetic (Dia) wild-type (WT) and CDA1BP1 KO (KO) mice were analyzed for their renal mRNA levels of collagen I, collagen III, fibronectin, and TNF-α. Representative images of the immunohistochemical staining (200×) for renal collagen III are shown in brown.
Diabetes Biomarker Network Analysis Suggests Perturbations Decades Before Diagnosis
A network analysis of 27 plasma biomarkers relating to diabetes development reveals a series of perturbations that occur in diabetes. According to Huang et al. (p. 281), certain changes happen decades before clinical diagnosis. Persistent dysregulation of insulin, HbA1c, C-peptide, and the leptin system appear to have central roles in diabetes development, at least when viewed from a network analysis perspective. The conclusions come from a secondary analysis of a case-control study nested in the Nurses’ Health Study (NHS) and involved analyzing a series of plasma biomarkers at baseline and then follow-up to identify case subjects with incident diabetes. The authors then constructed Spearman correlation networks based on factors that were statistically different between case and noncase subjects. Biomarkers representing glucose metabolism, inflammation, adipokines, endothelial function, and a range of other factors were included in the analysis. They identified 1,303 individuals with incident diabetes and matched them with 1,627 healthy control subjects. Based on the biochemical analyses, they found that leptin appeared to be the hub of the network with connections to five other biomarkers that differed significantly between case and noncase subjects. These included measures of adiponectin, C-reactive protein, HbA1c, and IGF binding protein 2. Leptin and insulin were also notable hubs. A notable part of the analysis is that in case subjects with diabetes diagnosed many years (>10 years) after blood collection, there was much perturbation in the biomarker correlation structure in comparison to case subjects diagnosed soon (<5 years) after blood collection and noncase subjects. Specifically, there were consistent differential correlations of insulin and HbA1c, with C-peptide the most highly connected node at early stages and leptin during mid-to-late stages. On that basis, they suggest that perturbations of diabetes-related biomarkers may start to happen decades prior to clinical recognition.
Biomarker correlation network comparing 1,303 case subjects with diabetes versus 1,627 noncase subjects without diabetes. ADPN, adiponectin; E-sel, E-selectin; HMW, high molecular weight.
Huang et al. A network analysis of biomarkers for type 2 diabetes. Diabetes 2019;68:281–290
Family Clustering of Rapid Renal Decline
A clinical feature of diabetic kidney disease, rapid renal decline, appears to cluster in certain families with diabetes, according to Frodsham et al. (p. 420). Specifically, the authors suggest that shared familial factors that are either environmental or genetic (or both) likely contribute to the rate of renal decline and diabetic kidney disease risk. First-degree relatives also had a marked increased risk of rapid renal decline. The conclusions come from a large retrospective cohort analysis involving individuals and families in the Utah Population Database (UPDB), which is a series of records that links electronic health records and genealogical records of the population of Utah. The authors calculated estimated glomerular filtration rate from creatinine data, as well as a range of other clinical measures, and then mapped the data to the genealogy records. In all, the authors managed to identify ∼15,000 individuals with diabetes with longitudinal creatinine measurements and who had follow-up of >1 year. They stratified the patients according to slow or rapid estimated glomerular filtration rate decline in the follow-up period. According to their report, they identified 2,127 individuals with rapid renal decline in 51 high-risk pedigrees. Discussing the findings, they state that due to the nature of the design of the study, they were unable to separate out exactly what factors might be at play in family pedigrees with rapid renal decline or excess diabetic kidney disease risk. Author Marcus G. Pezzolesi said: “By integrating a unique population-based genealogy resource with longitudinal electronic health record data, we’ve identified a number of high-risk pedigrees for rapid renal decline that will empower further studies aimed at identifying the factors that lead to this complication in patients with diabetes. Importantly, studying these families will allow us to accelerate discovery of novel factors, including genes and pathways, that contribute to rapid renal decline. We believe these discoveries will drive efforts to improve prevention and/or management of diabetic kidney disease.”
Frodsham et al. The familiarity of rapid renal decline in diabetes. Diabetes 2019;68:420–429
- © 2019 by the American Diabetes Association.
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