Insulin secretion hot spots in pancreatic ß cells as secreting adhesions.

Pancreatic ß cell secretion of insulin is crucial to the maintenance of glucose homeostasis and prevention of diseases related to glucose regulation, including diabetes. Pancreatic ß cells accomplish efficient insulin secretion by clustering secretion events at the cell membrane facing the vasculature. Regions at the cell periphery characterized by clustered secretion are currently termed insulin secretion hot spots. Several proteins, many associated with the microtubule and actin cytoskeletons, are known to localize to and serve specific functions at hot spots. Among these proteins are the scaffolding protein ELKS, the membrane-associated proteins LL5ß and liprins, the focal adhesion-associated protein KANK1, and other factors typically associated with the presynaptic active zone in neurons. These hot spot proteins have been shown to contribute to insulin secretion, but many questions remain regarding their organization and dynamics at hot spots. Current studies suggest microtubule- and F-actin are involved in regulation of hot spot proteins and their function in secretion. The hot spot protein association with the cytoskeleton networks also suggests a potential role for mechanical regulation of these proteins and hot spots in general. This perspective summarizes the existing knowledge of known hot spot proteins, their cytoskeletal-mediated regulation, and discuss questions remaining regarding mechanical regulation of pancreatic beta cell hot spots.

Machine Learning Models Identify New Inhibitors for Human OATP1B1.

The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [3H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug-drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.

Genome-wide CRISPR screen identified a role for commander complex mediated ITGB1 recycling in basal insulin secretion.

OBJECTIVES: Pancreatic beta cells secrete insulin postprandially and during fasting to maintain glucose homeostasis. Although glucose-stimulated insulin secretion (GSIS) has been extensively studied, much less is known about basal insulin secretion. Here, we performed a genome-wide CRISPR/Cas9 knockout screen to identify novel regulators of insulin secretion. METHODS: To identify genes that cell autonomously regulate insulin secretion, we engineered a Cas9-expressing MIN6 subclone that permits irreversible fluorescence labeling of exocytic insulin granules. Using a fluorescence-activated cell sorting assay of exocytosis in low glucose and high glucose conditions in individual cells, we performed a genome-wide CRISPR/Cas9 knockout screen. RESULTS: We identified several members of the COMMD family, a conserved family of proteins with central roles in intracellular membrane trafficking, as positive regulators of basal insulin secretion, but not GSIS. Mechanistically, we show that the Commander complex promotes insulin granules docking in basal state. This is mediated, at least in part, by its function in ITGB1 recycling. Defective ITGB1 recycling reduces its membrane distribution, the number of focal adhesions and cortical ELKS-containing complexes. CONCLUSIONS: We demonstrated a previously unknown function of the Commander complex in basal insulin secretion. We showed that by ITGB1 recycling, Commander complex increases cortical adhesions, which enhances the assembly of the ELKS-containing complexes. The resulting increase in the number of insulin granules near the plasma membrane strengthens basal insulin secretion.