We studied the ability of ATP to inhibit in vitro the degrading activity of insulin-degrading enzyme. The enzyme was purified from rat skeletal muscle by successive chromatographic steps. The last purification step showed two bands at 110 and 60 kDa in polyacrylamide gel. The enzyme was characterized by its insulin degradation activity, the substrate competition of unlabeled to labeled insulin, the profile of enzyme inhibitors, and the recognition by a specific antibody. One to 5 mM ATP induced a dose-dependent inhibition of insulin degradation (determined by trichloroacetic acid precipitation and insulin antibody binding). Inhibition by 3 mM adenosine 5'-diphosphate, adenosine 5'-monophosphate, guanosine 5'-triphosphate, pyrophosphate, beta-gamma-methyleneadenosine 5'-triphosphate, adenosine 5'-O-(3 thiotriphosphate), and dibutiryl cyclic adenosine 5'-monophosphate was 74%, 4%, 38%, 46%, 65%, 36%, and 0%, respectively, of that produced by 3 mM ATP. Kinetic analysis of ATP inhibition suggested an allosteric effect as the plot of 1/v (insulin degradation) versus ATP concentration was not linear and the Hill coefficient was more than 1 (1.51 and 2.44). The binding constant for allosteric inhibition was KiT = 1.5 x 10(-7) M showing a decrease of enzyme affinity induced by ATP. We conclude that ATP has an inhibitory effect on the insulin degradation activity of the enzyme.
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http://dx.doi.org/10.1177/153537020122600411 | DOI Listing |
J Agric Food Chem
January 2025
College of Food Science and Engineering, Northwest A&F University, Yangling, 712100 Shaanxi, China.
Pumpkin extract has been shown to alleviate hyperglycemic symptoms by improving glucose metabolism disorders. However, the specific active components responsible for its hypoglycemic effects and the underlying molecular mechanisms remain unclear. In this study, db/db mice underwent a 4-week dietary intervention with two pumpkin flours (PF1 and PF2), total dietary fiber (TDF), soluble dietary fiber (SDF), and insoluble dietary fiber (IDF), with acarbose serving as a positive control.
View Article and Find Full Text PDFiScience
January 2025
Section of Cell Biology and Functional Genomics, Department of Medicine, Endocrinology and Metabolism, Imperial College London, London, UK.
Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of beta cell function. Here, we show that an lncRNA-transcribed antisense to Pax6, annotated as Pax6os1/PAX6-AS1, was upregulated by high glucose concentrations in human as well as murine beta cell lines and islets. Elevated expression was also observed in islets from mice on a high-fat diet and patients with type 2 diabetes.
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January 2025
Lipids and Atherosclerosis Unit, Internal Medicine Unit, Reina Sofia University Hospital, 14004 Cordoba, Spain.
Alternative splicing is a post-transcriptional process resulting in multiple protein isoforms from a single gene. Abnormal splicing may lead to metabolic diseases, including type 2 diabetes mellitus (T2DM). To identify the splicing factor expression that predicts T2DM remission in coronary heart disease (CHD) patients, we identified newly diagnosed T2DM at baseline ( = 190) from the CORDIOPREV study.
View Article and Find Full Text PDFBrain Behav Immun Health
February 2025
Laboratory of Immuno-Endocrinology, Diabetes and Metabolism, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Pilar, Argentina.
Pancreatic β-cells are specialized in secreting insulin in response to circulating nutrients, mainly glucose. Diabetes is one of the most prevalent endocrine-metabolic diseases characterized by an imbalance in glucose homeostasis, which result mainly from lack of insulin production (type 1 diabetes) or insufficient insulin and peripheral insulin resistance (type 2 diabetes), both influenced by genetic and environmental components. Pancreatic β-cell dysfunction and islet inflammation are common characteristics of both types of the disease.
View Article and Find Full Text PDFDiabetes Metab Res Rev
January 2025
Division of Research, Kaiser Permanente Northern California, Pleasanton, California, USA.
Aims: Gestational diabetes mellitus (GDM) poses a significant risk for developing type 2 diabetes mellitus (T2D) and exhibits heterogeneity. However, understanding the link between different types of post-GDM individuals without diabetes and their progression to T2D is crucial to advance personalised medicine approaches.
Materials And Methods: We employed a discovery-based unsupervised machine learning clustering method to generate clustering models for analysing metabolomics, clinical, and biochemical datasets.
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