High levels of fucosylation and sialylation of milk N-glycans from mothers with gestational diabetes mellitus alter the offspring gut microbiome and immune balance in mice.
- 作者列表："Zhou J","Wang Y","Fan Q","Liu Y","Liu H","Yan J","Li M","Dong W","Li W
:Gestational diabetes mellitus (GDM) is significantly associated with allergen sensitization in early childhood, and this may influence the gut microbiome and immune system of the children. In addition to mother-to-child transmission of microbes, milk glycans play a pivotal role in shaping the gut microbiome of infants. A previous study has demonstrated alterations in the major milk N-glycans of mothers with GDM. However, the impact of these changes on the gut microbiome and immune response of the neonates has yet to be studied. Here, we aimed to compare the glycosylation levels of various milk glycans between normal and GDM mice, and to characterize the intestinal microbiome and immune responses of the offspring after weaning. We found that GDM mouse milk contained significantly higher concentrations of fucosylated and sialylated N-glycans than control mice, but there was no difference in the concentration of milk oligosaccharides between the groups. The differences in milk N-glycans had direct effects on the intestinal microbiome of the offspring, which in turn affected their immune response upon challenge with ovalbumin (OVA), with disruptions in the Th1/Th2 and Th17/Treg cell balances. This study lays the foundation for further research and development of specific nutritional care for the offspring of GDM mothers.
妊娠期糖尿病 (GDM) 与儿童早期过敏原致敏显著相关，这可能会影响儿童的肠道微生物组和免疫系统。除了微生物的母婴传播，乳聚糖在塑造婴儿的肠道微生物组中起着举足轻重的作用。先前的一项研究已经证明了 GDM 母亲的主要乳 N-聚糖的改变。然而，这些变化对新生儿肠道微生物组和免疫反应的影响还有待研究。在此，我们旨在比较正常和 GDM 小鼠之间各种乳聚糖的糖基化水平，并表征断奶后后代的肠道微生物组和免疫反应。我们发现 GDM 小鼠乳汁中岩藻糖基化和唾液酸化 N-聚糖的浓度明显高于对照小鼠，但各组间乳汁寡糖的浓度无差异。乳 N-聚糖的差异对后代的肠道微生物组有直接影响，这反过来又影响了它们在用卵清蛋白 (OVA) 攻击时的免疫反应, th1/Th2 和 Th17/Treg 细胞平衡中断。本研究为进一步研究和开发 GDM 母亲后代的特异性营养护理奠定基础。
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.