Glucocorticoids (GCs), such as prednisolone (PRED), are widely prescribed anti-inflammatory drugs, but their use may induce glucose intolerance and diabetes. GC-induced beta cell dysfunction contributes to these diabetogenic effects through mechanisms that remain to be elucidated. In this study, we hypothesized that activation of the unfolded protein response (UPR) following endoplasmic reticulum (ER) stress could be one of the underlying mechanisms involved in GC-induced beta cell dysfunction. We report here that PRED did not affect basal insulin release but time-dependently inhibited glucose-stimulated insulin secretion in INS-1E cells. PRED treatment also decreased both PDX1 and insulin expression, leading to a marked reduction in cellular insulin content. These PRED-induced detrimental effects were found to be prevented by prior treatment with the glucocorticoid receptor (GR) antagonist RU486 and associated with activation of two of the three branches of the UPR. Indeed, PRED induced a GR-mediated activation of both ATF6 and IRE1/XBP1 pathways but was found to reduce the phosphorylation of PERK and its downstream substrate eIF2α. These modulations of ER stress pathways were accompanied by upregulation of calpain 10 and increased cleaved caspase 3, indicating that long term exposure to PRED ultimately promotes apoptosis. Taken together, our data suggest that the inhibition of insulin biosynthesis by PRED in the insulin-secreting INS-1E cells results, at least in part, from a GR-mediated impairment in ER homeostasis which may lead to apoptotic cell death.
DOCUMENT
Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
DOCUMENT
Understanding taste is key for optimizing the palatability of seaweeds and other non-animal-based foods rich in protein. The lingual papillae in the mouth hold taste buds with taste receptors for the five gustatory taste qualities. Each taste bud contains three distinct cell types, of which Type II cells carry various G protein-coupled receptors that can detect sweet, bitter, or umami tastants, while type III cells detect sour, and likely salty stimuli. Upon ligand binding, receptor-linked intracellular heterotrimeric G proteins initiate a cascade of downstream events which activate the afferent nerve fibers for taste perception in the brain. The taste of amino acids depends on the hydrophobicity, size, charge, isoelectric point, chirality of the alpha carbon, and the functional groups on their side chains. The principal umami ingredient monosodium l-glutamate, broadly known as MSG, loses umami taste upon acetylation, esterification, or methylation, but is able to form flat configurations that bind well to the umami taste receptor. Ribonucleotides such as guanosine monophosphate and inosine monophosphate strongly enhance umami taste when l-glutamate is present. Ribonucleotides bind to the outer section of the venus flytrap domain of the receptor dimer and stabilize the closed conformation. Concentrations of glutamate, aspartate, arginate, and other compounds in food products may enhance saltiness and overall flavor. Umami ingredients may help to reduce the consumption of salts and fats in the general population and increase food consumption in the elderly.
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