尿石素 C 增加葡萄糖诱导的 ERK 激活，有助于胰岛素分泌。
- 作者列表："Toubal S","Oiry C","Bayle M","Cros G","Neasta J
:Polyphenols exert pharmacological actions through protein-mediated mechanisms and by modulating intracellular signalling pathways. We recently showed that a gut-microbial metabolite of ellagic acid named urolithin C is a glucose-dependent activator of insulin secretion acting by facilitating L-type Ca2+ channel opening and Ca2+ influx into pancreatic β-cells. However, it is still unknown whether urolithin C regulates key intracellular signalling proteins in β-cells. Here we report that urolithin C enhanced glucose-induced Extracellular Signal-Regulated Kinases 1/2 (ERK1/2) activation as shown by higher phosphorylation levels in INS-1 β-cells. Interestingly, inhibition of ERK1/2 with two structurally-distinct inhibitors led to a reduction of urolithin C effect on insulin secretion. Finally, we provide data to suggest that urolithin C-mediated ERK1/2 phosphorylation involved insulin signalling in INS-1 cells. Together, these data indicate that the pharmacological action of urolithin C on insulin secretion relies, in part, on its capacity to enhance glucose-induced ERK1/2 activation. Therefore, our study extends our understanding of the pharmacological action of urolithin C in β-cells. More generally, our findings revealed that urolithin C modulated the activation of key multifunctional intracellular signalling kinases which participate in the regulation of numerous biological processes.
: 多酚通过蛋白质介导的机制和调节细胞内信号通路发挥药理作用。我们最近发现鞣花酸的肠道微生物代谢产物 urolithin C 是一种葡萄糖依赖性胰岛素分泌激活剂，通过促进 L 型 Ca2 + 通道开放和 Ca2 + 流入胰腺起作用。 β 细胞。然而，urolithin C 是否调节 β-细胞中的关键细胞内信号蛋白仍然未知。这里我们报道了 urolithin C 增强葡萄糖诱导的细胞外信号调节激酶 1/2 (ERK1/2) 激活，如 INS-1 β 细胞中较高的磷酸化水平所示。有趣的是，用两种结构不同的抑制剂抑制 ERK1/2 可降低尿素 C 对胰岛素分泌的影响。最后，我们提供的数据表明，urolithin C 介导的 ERK1/2 磷酸化参与 INS-1 细胞的胰岛素信号传导。总之，这些数据表明，尿素 C 对胰岛素分泌的药理作用部分依赖于其增强葡萄糖诱导的 ERK1/2 活化的能力。因此，我们的研究扩展了我们对 urolithin C 在 β 细胞中的药理作用的理解。更一般地说，我们的研究结果揭示，urolithin C 调节了参与调控众多生物学过程的关键多功能细胞内信号激酶的激活。
METHODS:Aims We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. Methods A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). Results A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69–0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64–0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72–0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66–0.78), compared to an AUC of 0.67 (95% CI 0.63–0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62–0.70) using the Teede model. Conclusions A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
METHODS:Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy. Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^ 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^ 5 , 6 . Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.
METHODS::Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans Therefore, we sought to determine which of the currently approved sulfonylurea drugs prevent the release of interleukin 1β (IL-1β), a major inflammasome effector, during C. albicans challenge of the human macrophage-like THP1 cell line. Findings revealed that the second-generation antidiabetics (glyburide, glisoxepide, gliquidone, and glimepiride), which exhibit greater antidiabetic efficacy than prior iterations, demonstrated anti-inflammatory effects with various degrees of potency as determined by calculation of 50% inhibitory concentrations (IC50s). These same compounds were also effective in reducing IL-1β release during noninfectious inflammasome activation (e.g., induced by lipopolysaccharide [LPS] plus ATP), suggesting that their anti-inflammatory activity is not specific to C. albicans challenge. Moreover, treatment with sulfonylurea drugs did not impact C. albicans growth and filamentation or THP1 viability. Finally, the use of ECE1 and Candidalysin deletion mutants, along with isogenic NLRP3-/- cells, demonstrated that both Candidalysin and NLRP3 are required for IL-1β secretion, further confirming that sulfonylureas suppress inflammasome signaling. Moreover, challenge of THP1 cells with synthetic Candidalysin peptide demonstrated that this toxin is sufficient to activate the inflammasome. Treatment with the experimental inflammasome inhibitor MCC950 led to similar blockade of IL-1β release, suggesting that Candidalysin-mediated inflammasome activation can be inhibited independently of potassium efflux. Together, these results demonstrate that the second-generation antidiabetic sulfonylureas retain anti-inflammatory activity and may be considered for repurposing against immunopathological diseases, including vaginal candidiasis.