Screening and functional studies of long noncoding RNA in subjects with prediabetes.
糖尿病前期受试者长链非编码 RNA 的筛选和功能研究。
- 作者列表："Zhang P","Zhu X","Du Y","Dong Z","Qiao C","Li T","Chen P","Lou P
BACKGROUND:In recent years, long noncoding RNAs (LncRNAs) have been found to play an important role in type 2 diabetes mellitus. However, research on the relationship between LncRNAs and prediabetes is still emerging. OBJECTIVES:The study aim was to screen differently expressed LncRNAs and understand their localization and function in patients with prediabetes. METHODS:We used microarray analysis to screen LncRNAs in prediabetes participants.To further clarify the localization and function of the expressed mRNAs, we used gene ontology analysis and pathway analysis. Then, internal validations were performed using individual quantitative real-time polymerase chain reaction (qRT-PCR) assays. RESULTS:We identified 55 differently expressed LncRNAs and 36 mRNAs in prediabetes participants comparing with controls. Gene ontology analysis indicated that the most enriched transcript terms were multicellular organismal process, plasma membrane, and binding. Pathway analysis indicated that the differently expressed mRNAs were involved in processes such as starch and sucrose metabolism, pantothenate and coenzyme A biosynthesis, and nicotinate and nicotinamide metabolism. The qRT-PCR results showed a trend consistent with the microarray results in 30 patients and 30 healthy controls. CONCLUSIONS:We found aberrantly expressed LncRNAs and mRNAs in prediabetes subjects, and demonstrated that these LncRNAs are involved in the entire prediabetes biological process.
背景: 近年来，人们发现长链非编码 rna (long noncoding RNAs，LncRNAs) 在 2 型糖尿病中发挥重要作用。然而，关于 LncRNAs 与糖尿病前期之间关系的研究仍在不断涌现。 目的: 本研究旨在筛选不同表达的 LncRNAs，并了解它们在糖尿病前期患者中的定位和功能。 方法: 我们使用微阵列分析筛选糖尿病前期参与者的 LncRNAs。为了进一步明确表达的 mRNAs 的定位和功能，我们使用了基因本体论分析和通路分析。然后，使用个体定量实时聚合酶链反应 (qRT-PCR) 检测进行内部验证。 结果: 与对照组相比，我们在糖尿病前期参与者中鉴定出 55 个不同表达的 LncRNAs 和 36 个 mRNAs。基因本体论分析表明，最丰富的转录本术语为多细胞生物过程、质膜和结合。通路分析表明，不同表达的 mrna 参与了淀粉和蔗糖代谢、泛酸盐和辅酶a 生物合成以及烟酸盐和烟酰胺代谢等过程。在 30 例患者和 30 例健康对照中，qRT-PCR 结果显示与芯片结果一致的趋势。 结论: 我们在糖尿病前期受试者中发现了异常表达的 LncRNAs 和 mRNAs，并证明这些 LncRNAs 参与了整个糖尿病前期生物学过程。
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.