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| 2. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 3. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 8. |
- Shigeto, Makoto, et al.
(författare)
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GLP-1 stimulates insulin secretion by PKC-dependent TRPM4 and TRPM5 activation
- 2015
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Ingår i: Journal of Clinical Investigation. - : American Society for Clinical Investigation. - 0021-9738 .- 1558-8238. ; 125:12, s. 4714-4728
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Tidskriftsartikel (refereegranskat)abstract
- Strategies aimed at mimicking or enhancing the action of the incretin hormone glucagon-like peptide 1 (GLP-1) therapeutically improve glucose-stimulated insulin secretion (GSIS); however, it is not clear whether GLP-1 directly drives insulin secretion in pancreatic islets. Here, we examined the mechanisms by which GLP-1 stimulates insulin secretion in mouse and human islets. We found that GLP-1 enhances GSIS at a half-maximal effective concentration of 0.4 pM. Moreover, we determined that GLP-1 activates PLC, which increases submembrane diacylglycerol and thereby activates PKC, resulting in membrane depolarization and increased action potential firing and subsequent stimulation of insulin secretion. The depolarizing effect of GLP-1 on electrical activity was mimicked by the PKC activator PMA, occurred without activation of PKA, and persisted in the presence of PKA inhibitors, the K-ATP channel blacker tolbutamide, and the L-type Ca2+ channel blacker isradipine; however, depolarization was abolished by lowering extracellular Na+. The PKC-dependent effect of GLP-1 on membrane potential and electrical activity was mediated by activation of NW-permeable TRPM4 and TRPM5 channels by mobilization of intracellular Ca2+ from thapsigargin-sensitive Ca2+ stores. Concordantly, GLP-1 effects were negligible in Trpm4 or Trpm5 KO islets. These data provide important insight into the therapeutic action of GLP-1 and suggest that circulating levels of this hormone directly stimulate insulin secretion by beta cells.
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| 9. |
- Tarasov, A. I., et al.
(författare)
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Monitoring real-time hormone release kinetics: Via high-content 3-D imaging of compensatory endocytosis
- 2018
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Ingår i: Lab on a Chip. - : Royal Society of Chemistry (RSC). - 1473-0197 .- 1473-0189. ; 18:18, s. 2838-2848
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Tidskriftsartikel (refereegranskat)abstract
- High-content real-time imaging of hormone secretion in tissues or cell populations is a challenging task, which is unlikely to be resolved directly, despite immense translational value. We approach this problem indirectly, using compensatory endocytosis, a process that closely follows exocytosis in the cell, as a surrogate read-out for secretion. The tissue is immobilized in an open-air perifusion chamber and imaged using a two-photon microscope. A fluorescent polar tracer, perifused through the experimental circuit, gets trapped into the cells via endocytosis, and is quantified using a feature-detection algorithm. The signal of the tracer that accumulates into the endocytotic system reliably reflects stimulated exocytosis, which is demonstrated via co-imaging of the latter using existing reporters. A high signal-to-noise ratio and compatibility with multisensor imaging affords the real-time quantification of the secretion at the tissue/population level, whereas the cumulative nature of the signal allows imprinting of the “secretory history” within each cell. The technology works for several cell types, reflects disease progression and can be used for human tissue.
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| 10. |
- Briant, L. J. B., et al.
(författare)
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Functional identification of islet cell types by electrophysiological fingerprinting
- 2017
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Ingår i: Journal of the Royal Society Interface. - : The Royal Society. - 1742-5689 .- 1742-5662. ; 14:128
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Tidskriftsartikel (refereegranskat)abstract
- The alpha-, beta- and delta-cells of the pancreatic islet exhibit different electrophysiological features. We used a large dataset of whole- cell patch- clamp recordings from cells in intactmouse islets (N = 288 recordings) to investigatewhether it is possible to reliably identify cell type (alpha,beta or gamma) based on their electrophysiological characteristics. We quantified 15 electrophysiological variables in each recorded cell. Individually, none of the variables could reliably distinguish the cell types. We therefore constructed a logistic regressionmodel that included all quantified variables, to determine whether they could together identify cell type. The model identified cell typewith 94% accuracy. Thismodelwas applied to a dataset of cells recorded from hyperglycaemic bV59M mice; it correctly identified cell type in all cells and was able to distinguish cells that co-expressed insulin and glucagon. Based on this revised functional identification, we were able to improve conductance-based models of the electrical activity in alpha-cells and generate a model of gamma-cell electrical activity. These new models could faithfully emulate alpha- and gamma-cell electrical activity recorded experimentally.
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