Exposure to Gestational Diabetes Enriches Immune-Related Pathways in the Transcriptome and Methylome of Human Amniocytes

Hesperidin inhibits methylation and autophagy in LPS and high glucose-induced human villous trophoblasts

INTRODUCTION

Gestational diabetes mellitus (GDM) is the first occurrence of diabetes due to abnormal maternal sugar metabolism after pregnancy, which may lead to adverse pregnancy outcomes. Hesperidin is known to decrease in the cord blood of GDM with obesity, but its role is unknown. This study aims to explore the potential function of hesperidin in GDM with obesity to develop new therapeutic ideas.

METHODS

Peripheral blood and placental tissues from GDM and GDM with obesity patients were collected to isolate human villous trophoblasts and detection. Bioinformatics was used to analyze the differential methylation genes between GDM and GDM with obesity. Immunofluorescence was applied for the detection of CK7 expression. Cells vitality was detected by CCK8 and transwell. Molecular docking was applied to predict the binding of hesperidin and ATG7 protein. Inflammation and m6A levels was analyzed by ELISA. ATG7, LC3, TLR4 and P62 proteins was analyzed by Western blot.

RESULTS

The methylation of ATG7 gene was up-regulated in GDM with obesity compared with GDM. The m6A and autophagy proteins levels in GDM with obesity were higher than that in GDM. LPS with 2.5-25 mM glucose induced the increase of autophagy proteins, inflammation and m6A levels in human villous trophoblasts. Hesperidin formed hydrogen bonds and hydrophobic interactions with ATG7 proteins. Hesperidin (0.25 μM) inhibited the autophagy proteins and m6A level in LPS and 25 mM glucose-induced human villous trophoblasts.

DISCUSSION

GDM with obesity followed the increase of autophagy proteins and m6A levels. Hesperidin inhibited the autophagy proteins and m6A level in LPS and glucose-induced human villous trophoblasts.

Impact of intrauterine exposure to maternal diabetes on preterm birth: fetal DNA methylation alteration is an important mediator

BACKGROUND

In utero exposure to diabetes has been shown to contribute to preterm birth, though the underlying biological mechanisms are yet to be fully elucidated. Fetal epigenetic variations established in utero may be a possible pathway. This study aimed to investigate whether in utero exposure to diabetes was associated with a change in newborn DNA methylation, and whether the identified CpG sites mediate the association between diabetes and preterm birth in a racially diverse birth cohort population.

METHODS

This study included 954 mother-newborn pairs. Methylation levels in the cord blood were determined using the Illumina Infinium MethylationEPIC BeadChip 850 K array platform. In utero exposure to diabetes was defined by the presence of maternal pregestational or gestational diabetes. Preterm birth was defined as gestational age at birth less than 37 weeks. Linear regression analysis was employed to identify differentially methylated CpG sites. Differentially methylated regions were identified using the DMRcate Package.

RESULTS

126 (13%) newborns were born to mothers with diabetes in pregnancy and 173 (18%) newborns were born preterm, while 41 newborns were born both preterm and to mothers with diabetes in pregnancy. Genomic-wide CpG analysis found that eighteen CpG sites in cord blood were differentially methylated by maternal diabetes status at an FDR threshold of 5%. These significant CpG sites were mapped to 12 known genes, one of which was annotated to gene Major Histocompatibility Complex, Class II, DM Beta (HLA-DMB). Consistently, one of the two identified significant methylated regions overlapped with HLA-DMB. The identified differentially methylated CpG sites mediated the association between diabetes in pregnancy and preterm birth by 61%.

CONCLUSIONS

In this US birth cohort, we found that maternal diabetes was associated with altered fetal DNA methylation patterns, which substantially explained the link between diabetes and preterm birth.

The link between gestational diabetes and cardiovascular diseases: potential role of extracellular vesicles

Extracellular vesicles are critical mediators of cell communication. They encapsulate a variety of molecular cargo such as proteins, lipids, and nucleic acids including miRNAs, lncRNAs, circular RNAs, and mRNAs, and through transfer of these molecular signals can alter the metabolic phenotype in recipient cells. Emerging studies show the important role of extracellular vesicle signaling in the development and progression of cardiovascular diseases and associated risk factors such as type 2 diabetes and obesity. Gestational diabetes mellitus (GDM) is hyperglycemia that develops during pregnancy and increases the future risk of developing obesity, impaired glucose metabolism, and cardiovascular disease in both the mother and infant. Available evidence shows that changes in maternal metabolism and exposure to the hyperglycemic intrauterine environment can reprogram the fetal genome, leaving metabolic imprints that define life-long health and disease susceptibility. Understanding the factors that contribute to the increased susceptibility to metabolic disorders of children born to GDM mothers is critical for implementation of preventive strategies in GDM. In this review, we discuss the current literature on the fetal programming of cardiovascular diseases in GDM and the impact of extracellular vesicle (EV) signaling in epigenetic programming in cardiovascular disease, to determine the potential link between EV signaling in GDM and the development of cardiovascular disease in infants.

Diabetes mellitus susceptibility with varied diseased phenotypes and its comparison with phenome interactome networks

An emerging area of interest in understanding disease phenotypes is systems genomics. Complex diseases such as diabetes have played an important role towards understanding the susceptible genes and mutations. A wide number of methods have been employed and strategies such as polygenic risk score and allele frequencies have been useful, but understanding the candidate genes harboring those mutations is an unmet goal. In this perspective, using systems genomic approaches, we highlight the application of phenome-interactome networks in diabetes and provide deep insights. LINC01128, which we previously described as candidate for diabetes, is shown as an example to discuss the approach.

DLRAPom: a hybrid pipeline of Optimized XGBoost-guided integrative multiomics analysis for identifying targetable disease-related lncRNA-miRNA-mRNA regulatory axes

The lack of a reliable and easy-to-operate screening pipeline for disease-related noncoding RNA regulatory axis is a problem that needs to be solved urgently. To address this, we designed a hybrid pipeline, disease-related lncRNA-miRNA-mRNA regulatory axis prediction from multiomics (DLRAPom), to identify risk biomarkers and disease-related lncRNA-miRNA-mRNA regulatory axes by adding a novel machine learning model on the basis of conventional analysis and combining experimental validation. The pipeline consists of four parts, including selecting hub biomarkers by conventional bioinformatics analysis, discovering the most essential protein-coding biomarkers by a novel machine learning model, extracting the key lncRNA-miRNA-mRNA axis and validating experimentally. Our study is the first one to propose a new pipeline predicting the interactions between lncRNA and miRNA and mRNA by combining WGCNA and XGBoost. Compared with the methods reported previously, we developed an Optimized XGBoost model to reduce the degree of overfitting in multiomics data, thereby improving the generalization ability of the overall model for the integrated analysis of multiomics data. With applications to gestational diabetes mellitus (GDM), we predicted nine risk protein-coding biomarkers and some potential lncRNA-miRNA-mRNA regulatory axes, which all correlated with GDM. In those regulatory axes, the MALAT1/hsa-miR-144-3p/IRS1 axis was predicted to be the key axis and was identified as being associated with GDM for the first time. In short, as a flexible pipeline, DLRAPom can contribute to molecular pathogenesis research of diseases, effectively predicting potential disease-related noncoding RNA regulatory networks and providing promising candidates for functional research on disease pathogenesis.

Decreased Monocyte Count Is Associated With Gestational Diabetes Mellitus Development, Macrosomia, and Inflammation

CONTEXT

The immune system plays a central role in the pathophysiology of gestational diabetes mellitus (GDM). Monocytes, the main innate immune cells, are especially important in the maintenance of a normal pregnancy.

OBJECTIVE

Here, we investigated the potential effect of monocytes in GDM.

METHODS

Monocyte count was monitored throughout pregnancy in 214 women with GDM and 926 women without in a case-control and cohort study. Circulating levels of inflammatory cytokines, placenta-derived macrophages, and their products were measured.

RESULTS

Throughout pregnancy, monocyte count was significantly decreased in women with GDM, and was closely associated with glucose level, insulin resistance, and newborn weight. First-trimester monocyte count outperformed that of the second and third trimester as a risk factor and diagnostic predictor of GDM and macrosomia both in the case-control and cohort study. In addition, our cohort study showed that as first-trimester monocyte count decreased, GDM and macrosomia incidence, glucose level, and newborn weight increased in a stepwise manner. Risk of GDM started to decrease rapidly when first-trimester monocyte count exceeded 0.48 × 109/L. Notably, CD206 and interleukin 10 (IL-10) were significantly lower, whereas CD80, CD86, tumor necrosis factor α (TNF-α), and interleukin 6 (IL-6) were higher both in GDM placental tissue and peripheral blood. First-trimester monocyte count was positively related to IL-10 and CD206, but negatively related to CD80, CD86, TNF-α, and IL-6.

CONCLUSION

Decreased monocyte count throughout pregnancy was closely associated with the development of GDM, macrosomia, and the chronic inflammatory state of GDM. First-trimester monocyte count has great potential as an early diagnostic marker of GDM.

Epigenetic Changes in Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is defined as carbohydrate intolerance that appears or is for the first time diagnosed during pregnancy. It can lead to many complications in the mother and in the offspring, so diagnostics and management of GDM are important to avoid adverse pregnancy outcomes. Epigenetic studies revealed the different methylation status of genes in pregnancies with GDM compared to pregnancies without GDM. A growing body of evidence shows that the GDM can affect not only the course of the pregnancy, but also the development of the offspring, thus contributing to long-term effects and adverse health outcomes of the progeny. Epigenetic changes occur through histone modification, DNA methylation, and disrupted function of non-coding ribonucleic acid (ncRNA) including microRNAs (miRNAs). In this review, we focus on the recent knowledge about epigenetic changes in GDM. The analysis of this topic may help us to understand pathophysiological mechanisms in GDM and find a solution to prevent their consequences.

Current State of Diabetes Mellitus Prevalence, Awareness, Treatment, and Control in Latin America: Challenges and Innovative Solutions to Improve Health Outcomes Across the Continent

PURPOSE OF REVIEW

Latin America is the scenario of great inequalities where about 32 million human beings live with diabetes. Through this review, we aimed at describing the current state of the prevalence, awareness, treatment, and control of diabetes mellitus and completion of selected guidelines of care across Latin America and identify opportunities to advance research that promotes better health outcomes.

RECENT FINDINGS

The prevalence of diabetes mellitus has been consistently increasing across the region, with some variation: higher prevalence in Mexico, Haiti, and Puerto Rico and lower in Colombia, Ecuador, Dominican Republic, Peru, and Uruguay. Prevalence assessment methods vary, and potentially underestimating the real number of persons with diabetes. Diabetes unawareness varies widely, with up to 50% of persons with diabetes who do not know they may have the disease. Glycemic, blood pressure, and LDL-C control and completion of guidelines to prevent microvascular complications are not consistently assessed across studies, and the achievement of control goals is suboptimal. On the other hand, multiple interventions, point-of-care/rapid assessment tools, and alternative models of health care delivery have been proposed and tested throughout Latin America. The prevalence of diabetes mellitus continues to rise across Latin America, and the number of those with the disease may be underestimated. However, some local governments are embedding more comprehensive diabetes assessments in their local national surveys. Clinicians and public health advocates in the region have proposed and initiated various multi-level interventions to address this enormous challenge in the region.