Gene expression changes in arterial and venous endothelial cells exposed to gestational diabetes mellitus.
- 作者列表："Liu Y","Wang Y","Wang Y","Lv Y","Zhang Y","Wang H
:We investigated the molecular changes in fetoplacental blood vessel endothelial cells in gestational diabetes mellitus (GDM). Raw gene expression profile data of arterial and venous endothelial cells from GDM complicated pregnancies and healthy controls were downloaded and used for bioinformatic analysis. There were two differentially expressed genes (DEGs) in venous endothelial cells and 178 DEGs in arterial endothelial cells induced by GDM. The altered genes were clustered to pathways associated with cell cycle, p53 signaling pathway, and cellular senescence. The disease associated gene-pathway network that was constructed comprised eight down-regulated genes (including FBXO5, CCNB1, and CDK1), one up-regulated gene (CCND2), hsa04068: FoxO signaling pathway and hsa04114: Oocyte mitosis pathway. CCND2 was a significant node in the microRNA (miRNA)-target network, which was regulated by seven miRNAs that included hsa-miR-1299, hsa-miR-1200, and hsa-miR-miR-593-5p. FBXO5 was a significant node regulated by two miRNAs. CCND2 and FBXO5 were also the significant nodes in the transcriptional factors-target network and integrated regulatory network. The cell cycle pathway was significantly altered in arterial endothelial cells during GDM, which was involved with the differential expression of CCND2 and FBXO5.
: 我们研究了妊娠期糖尿病 (GDM) 胎儿胎盘血管内皮细胞的分子变化。下载来自 GDM 复杂妊娠和健康对照的动脉和静脉内皮细胞的原始基因表达谱数据，并用于生物信息学分析。GDM 诱导的静脉内皮细胞有 2 个差异表达基因 (DEGs)，动脉内皮细胞有 178 个 DEGs。改变的基因聚集到与细胞周期、 p53 信号通路和细胞衰老相关的通路。构建的疾病相关基因通路网络由 8 个下调基因 (包括 FBXO5 、 CCNB1 和 CDK1) 、 1 个上调基因 (CCND2) 、 hsa04068 组成: foxO 信号通路和 hsa04114: 卵母细胞有丝分裂通路。CCND2 是 microRNA (miRNA)-target 网络中的一个重要节点，由 7 个 miRNA 调控，包括 hsa-miR-1299 、 hsa-miR-1200 和 hsa-miR-miR-593-5p。FBXO5 是由两个 mirna 调控的显著节点。CCND2 和 FBXO5 也是转录因子-靶标网络和整合调控网络中的显著节点。GDM 期间动脉内皮细胞细胞周期通路明显改变，这与 CCND2 和 fbxo5 的差异表达有关。
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.