Identification of Potential Therapeutic Targets and Pathways of Liraglutide Against Type 2 Diabetes Mellitus (T2DM) Based on Long Non-Coding RNA (lncRNA) Sequencing.
基于长链非编码 RNA (lncRNA) 测序鉴定利拉鲁肽抗 2 型糖尿病 (T2DM) 的潜在治疗靶点和通路。
- 作者列表："Huang Y","Li J","Chen S","Zhao S","Huang J","Zhou J","Xu Y
:BACKGROUND The aim of this study was to explore the potential therapeutic targets and pathways of liraglutide against type 2 diabetes mellitus (T2DM) in streptozotocin-induced diabetic rats based on lncRNA sequencing. MATERIAL AND METHODS Male Wistar rats were randomly divided into 3 groups: the control group (n=10), the T2DM model group (high-sugar and high-fat diet, and streptozotocin-induced, n=11), and the liraglutide group (model plus liraglutide, n=10). After 8 weeks of drug treatment, lncRNA sequencing was used to identify the lncRNA therapeutic targets and their related protein-coding genes of liraglutide against T2DM, which were further studied by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to determine the major biological processes and pathways involved in the action of liraglutide treatment. Lastly, several lncRNA targets were randomly detected based on quantitative real-time polymerase chain reaction (QRT-PCR) to verify the accuracy of sequencing results. RESULTS A total of 104 lncRNA targets of liraglutide against T2DM were screened, with 27 upregulated and 77 downregulated, including NONRATT030354.2, MSTRG.1456.6, and NONRATT011758.2. The major biological processes involved were glucose and lipid metabolism and amino acid metabolism. Liraglutide had a therapeutic effect in T2DM, mainly through the Wnt, PPAR, amino acid metabolism signaling, mTOR, and lipid metabolism-related pathways. CONCLUSIONS In this study, we screened 104 lncRNA therapeutic targets and several signaling pathways (Wnt, PPAR, amino acid metabolism signaling pathway, mTOR, and lipid metabolism-related pathways) of liraglutide against T2DM based on lncRNA sequencing.
背景: 本研究的目的是基于 lncRNA 测序，在链脲佐菌素诱导的糖尿病大鼠中探索利拉鲁肽抗 2 型糖尿病 (T2DM) 的潜在治疗靶点和通路。材料与方法雄性 Wistar 大鼠随机分为 3 组: 对照组 (n = 10) 、 T2DM 模型组 (高糖高脂饮食、和链脲佐菌素诱导，n = 11) 和利拉鲁肽组 (模型加利拉鲁肽，n = 10)。药物治疗 8 周后，采用 lncRNA 测序鉴定利拉鲁肽抗 T2DM 的 lncRNA 治疗靶点及其相关蛋白编码基因，并通过 Gene Ontology (GO) 进一步研究和京都基因和基因组百科全书 (KEGG)富集分析，确定参与利拉鲁肽处理作用的主要生物过程和途径。最后，基于定量实时聚合酶链反应 (QRT-PCR) 随机检测几个 lncRNA 靶标，以验证测序结果的准确性。结果共筛选出 104 个利拉鲁肽抗 T2DM 的 lncRNA 靶点，上调 27 个，下调 77 个，包括 NONRATT030354.2 、 MSTRG.1456.6 和 nonratt011758.2。参与的主要生物学过程为糖脂代谢和氨基酸代谢。利拉鲁肽对 T2DM 有治疗作用，主要通过 Wnt 、 PPAR 、氨基酸代谢信号、 mTOR 和脂代谢相关通路。结论在本研究中，我们筛选了 104 个 lncRNA 治疗靶点和几个信号通路 (Wnt 、 PPAR 、氨基酸代谢信号通路、 mTOR 和脂质代谢相关通路) 基于 lncRNA 测序的利拉鲁肽抗 T2DM。
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.