Subthreshold α2-Adrenergic Activation Counteracts Glucagon-Like Peptide-1 Potentiation of Glucose-Stimulated Insulin Secretion

The pancreatic β cell harbors α2-adrenergic and glucagon-like peptide-1 (GLP-1) receptors on its plasma membrane to sense the corresponding ligands adrenaline/noradrenaline and GLP-1 to govern glucose-stimulated insulin secretion. However, it is not known whether these two signaling systems interact to gain the adequate and timely control of insulin release in response to glucose. The present work shows that the α2-adrenergic agonist clonidine concentration-dependently depresses glucose-stimulated insulin secretion from INS-1 cells. On the contrary, GLP-1 concentration-dependently potentiates insulin secretory response to glucose. Importantly, the present work reveals that subthreshold α2-adrenergic activation with clonidine counteracts GLP-1 potentiation of glucose-induced insulin secretion. This counteractory process relies on pertussis toxin- (PTX-) sensitive Gi proteins since it no longer occurs following PTX-mediated inactivation of Gi proteins. The counteraction of GLP-1 potentiation of glucose-stimulated insulin secretion by subthreshold α2-adrenergic activation is likely to serve as a molecular mechanism for the delicate regulation of insulin release

Enhanced expression of beta cell Ca(v)3.1 channels impairs insulin release and glucose homeostasis

Voltage-gated calcium 3.1 (Ca(v)3.1) channels are absent in healthy mouse beta cells and mediate minor T-type Ca2+ currents in healthy rat and human beta cells but become evident under diabetic conditions. Whether more active Ca(v)3.1 channels affect insulin secretion and glucose homeostasis remains enigmatic. We addressed this question by enhancing de novo expression of beta cell Ca(v)3.1 channels and exploring the consequent impacts on dynamic insulin secretion and glucose homeostasis as well as underlying molecular mechanisms with a series of in vitro and in vivo approaches. We now demonstrate that a recombinant adenovirus encoding enhanced green fluorescent protein-Ca(v)3.1 subunit (Ad-EGFP-Ca(v)3.1) efficiently transduced rat and human islets as well as dispersed islet cells. The resulting Ca(v)3.1 channels conducted typical T-type Ca2+ currents, leading to an enhanced basal cytosolic-free Ca2+ concentration ([Ca2+](i)). Ad-EGFP-Ca(v)3.1-transduced islets released significantly less insulin under both the basal and first phases following glucose stimulation and could no longer normalize hyperglycemia in recipient rats rendered diabetic by streptozotocin treatment. Furthermore, Ad-EGFP-Ca(v)3.1 transduction reduced phosphorylated FoxO1 in the cytoplasm of INS-1E cells, elevated FoxO1 nuclear retention, and decreased syntaxin 1A, SNAP-25, and synaptotagmin III. These effects were prevented by inhibiting Ca(v)3.1 channels or the Ca2+ -dependent phosphatase calcineurin. Enhanced expression of beta cell Ca(v)3.1 channels therefore impairs insulin release and glucose homeostasis by means of initial excessive Ca2+ influx, subsequent activation of calcineurin, consequent dephosphorylation and nuclear retention of FoxO1, and eventual FoxO1-mediated down-regulation of beta cell exocytotic proteins. The present work thus suggests an elevated expression of Ca(v)3.1 channels plays a significant role in diabetes pathogenesis

Inositol hexakisphosphate primes syndapin I/PACSIN 1 activation in endocytosis

Endocytosis is controlled by a well-orchestrated molecular machinery, where the individual players as well as their precise interactions are not fully understood. We now show that syndapin I/PACSIN 1 is expressed in pancreatic β cells and that its knockdown abrogates β cell endocytosis leading to disturbed plasma membrane protein homeostasis, as exemplified by an elevated density of L-type Ca(2+) channels. Intriguingly, inositol hexakisphosphate (InsP(6)) activates casein kinase 2 (CK2) that phosphorylates syndapin I/PACSIN 1, thereby promoting interactions between syndapin I/PACSIN 1 and neural Wiskott–Aldrich syndrome protein (N-WASP) and driving β cell endocytosis. Dominant-negative interference with endogenous syndapin I/PACSIN 1 protein complexes, by overexpression of the syndapin I/PACSIN 1 SH3 domain, decreases InsP(6)-stimulated endocytosis. InsP(6) thus promotes syndapin I/PACSIN 1 priming by CK2-dependent phosphorylation, which endows the syndapin I/PACSIN 1 SH3 domain with the capability to interact with the endocytic machinery and thereby initiate endocytosis, as exemplified in β cells. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00018-022-04305-2

Experimental Realization of an Extreme-Parameter Omnidirectional Cloak

An ideal transformation-based omnidirectional cloak always relies on metamaterials with extreme parameters, which were previously thought to be too difficult to realize. For such a reason, in previous experimental proposals of invisibility cloaks, the extreme parameters requirements are usually abandoned, leading to inherent scattering. Here, we report on the first experimental demonstration of an omnidirectional cloak that satisfies the extreme parameters requirement, which can hide objects in a homogenous background. Instead of using resonant metamaterials that usually involve unavoidable absorptive loss, the extreme parameters are achieved using a nonresonant metamaterial comprising arrays of subwavelength metallic channels manufactured with 3D metal printing technology. A high level transmission of electromagnetic wave propagating through the present omnidirectional cloak, as well as significant reduction of scattering field, is demonstrated both numerically and experimentally. Our work may also inspire experimental realizations of the other full-parameter omnidirectional optical devices such as concentrator, rotators, and optical illusion apparatuses

Intracameral Microimaging of Maturation of Human iPSC Derivatives into Islet Endocrine Cells

We exploited the anterior chamber of the eye (ACE) of immunodeficient mice as an ectopic site for both transplantation and microimaging of engineered surrogate islets from human induced pluripotent stem cells (hiPSC-SIs). These islets contained a majority of insulin-expressing cells, positive or negative for PDX1 and NKX6.1, and a minority of glucagon- or somatostatin-positive cells. Single, non-aggregated hiPSC-SIs were satisfactorily engrafted onto the iris. They underwent gradual vascularization and progressively increased their light scattering signals, reflecting the abundance of zinc-insulin crystal packaged inside mature insulin secretory granules. Intracameral hiPSC-SIs retrieved from recipients showed enhanced insulin immunofluorescence in correlation with the parallel increase in overall vascularization and light backscattering during the post-transplantation period. This approach enables longitudinal, nondestructive and intravital microimaging of cell fates, engraftment, vascularization and mature insulin secretory granules of single hiPSC-SI grafts, and may offer a feasible and reliable means to screen compounds for promoting in vivo hiPSC-SI maturation

Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm

AIM: To conduct a classification study of high myopic maculopathy (HMM) using limited datasets, including tessellated fundus, diffuse chorioretinal atrophy, patchy chorioretinal atrophy, and macular atrophy, and minimize annotation costs, and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification. METHODS: The optimized ALFA-Mix algorithm (ALFA-Mix+) was compared with five algorithms, including ALFA-Mix. Four models, including ResNet18, were established. Each algorithm was combined with four models for experiments on the HMM dataset. Each experiment consisted of 20 active learning rounds, with 100 images selected per round. The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+ outperformed other algorithms. Finally, this study employed six models, including EfficientFormer, to classify HMM. The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+ algorithm to achieve satisfactory classification results with a small dataset. RESULTS: ALFA-Mix+ outperforms other algorithms with an average superiority of 16.6, 14.75, 16.8, and 16.7 rounds in terms of accuracy, sensitivity, specificity, and Kappa value, respectively. This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images. The EfficientFormer achieved the best results with an accuracy, sensitivity, specificity, and Kappa value of 0.8821, 0.8334, 0.9693, and 0.8339, respectively. Therefore, by combining ALFA-Mix+ with EfficientFormer, this study achieved results with an accuracy, sensitivity, specificity, and Kappa value of 0.8964, 0.8643, 0.9721, and 0.8537, respectively. CONCLUSION: The ALFA-Mix+ algorithm reduces the required samples without compromising accuracy. Compared to other algorithms, ALFA-Mix+ outperforms in more rounds of experiments. It effectively selects valuable samples compared to other algorithms. In HMM classification, combining ALFA-Mix+ with EfficientFormer enhances model performance, further demonstrating the effectiveness of ALFA-Mix+