Immune checkpoint molecule PD-1 acts as a novel biomarker for the pathological process of gestational diabetes mellitus

Phenotypic characterisation of regulatory T cells in patients with gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a common complication that occurs during pregnancy. Emerging evidence suggests that immune abnormalities play a pivotal role in the development of GDM. Specifically, regulatory T cells (Tregs) are considered a critical factor in controlling maternal-fetal immune tolerance. However, the specific characteristics and alterations of Tregs during the pathogenesis of GDM remain poorly elucidated. Therefore, this study aimed to investigate the changes in Tregs among pregnant women diagnosed with GDM compared to healthy pregnant women. A prospective study was conducted, enrolling 23 healthy pregnant women in the third trimester and 21 third-trimester women diagnosed with GDM. Participants were followed up until the postpartum period. The proportions of various Treg, including Tregs, mTregs, and nTregs, were detected in the peripheral blood of pregnant women from both groups. Additionally, the expression levels of PD-1, HLA-G, and HLA-DR on these Tregs were examined. The results revealed no significant differences in the proportions of Tregs, mTregs, and nTregs between the two groups during the third trimester and postpartum period. However, GDM patients exhibited significantly reduced levels of PD-1+ Tregs (P < 0.01) and HLA-G+ Tregs (P < 0.05) in the third trimester compared to healthy pregnant women in the third trimester. Furthermore, GDM patients demonstrated significantly lower levels of PD-1+ mTregs (P < 0.01) and HLA-G+ (P < 0.05) mTregs compared to healthy pregnant women in the third trimester. Overall, the proportion of Tregs did not exhibit significant changes during the third trimester in GDM patients compared to healthy pregnant women. Nevertheless, the observed dysregulation of immune regulation function in Tregs and mTregs may be associated with the development of GDM in pregnant women.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Immune checkpoint inhibitors and reproductive failures

The human conceptus is a semi-allograft, which is antigenically foreign to the mother. Hence, the implantation process needs mechanisms to prevent allograft rejection during successful pregnancy. Immune checkpoints are a group of inhibitory pathways expressed on the surface of various immune cells in the form of ligand receptors. Immune cells possess these pathways to regulate the magnitude of immune responses and induce maternal-fetal tolerance. Briefly, 1) CTLA-4 can weaken T cell receptor (TCR) signals and inhibit T cell response; 2) The PD-1/PD-L1 pathway can reduce T cell proliferation, enhance T cell anergy and fatigue, reduce cytokine production, and increase T regulatory cell activity to complete the immunosuppression; 3) TIM3 interacts with T cells by binding Gal-9, weakening Th1 cell-mediated immunity and T cell apoptosis; 4) The LAG-3 binding to MHC II can inhibit T cell activation by interfering with the binding of CD4 to MHC II, and; 5) TIGIT can release inhibitory signals to NK and T cells through the ITIM structure of its cytoplasmic tail. Therefore, dysregulated immune checkpoints or the application of immune checkpoint inhibitors may impair human reproduction. This review intends to deliver a comprehensive overview of immune checkpoints in pregnancy, including CTLA-4, PD-1/PD-L1, TIM-3, LAG-3, TIGIT, and their inhibitors, reviewing their roles in normal and pathological human pregnancies.

Increased Pro-Inflammatory T Cells, Senescent T Cells, and Immune-Check Point Molecules in the Placentas of Patients With Gestational Diabetes Mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) is the most common metabolic complication of pregnancy. To define the altered pathway in GDM placenta, we investigated the transcriptomic profiles from human placenta between GDM and controls.

METHODS

Clinical parameters and postpartum complications were reviewed in all participants. Differentially expressed canonical pathways were analyzed between the GDM and control groups based on transcriptomic analysis. CD4⁺ T, CD8⁺ T, and senescent T cell subsets were determined by flow cytometry based on staining for specific intracellular cytokines.

RESULTS

Gene ontology analysis revealed that the placenta of GDM revealed upregulation of diverse mitochondria or DNA replication related pathways and downregulation of T-cell immunity related pathways. The maternal placenta of the GDM group had a higher proportion of CD4⁺ T and CD8⁺ T cells than the control group. Interestingly, senescent CD4⁺ T cells tended to increase and CD8⁺ T cells were significantly increased in GDM compared to controls, along with increased programmed cell death-1 (CD274⁺) expression. Programmed death-ligand 1 expression in syncytotrophoblasts was also significantly increased in patients with GDM.

CONCLUSION

This study demonstrated increased proinflammatory T cells, senescent T cells and immune-check point molecules in GDM placentas, suggesting that changes in senescent T cells and immune-escape signaling might be related to the pathophysiology of GDM.

Identification of diagnostic cytosine-phosphate-guanine biomarkers in patients with gestational diabetes mellitus via epigenome-wide association study and machine learning

OBJECTIVE

To explore gestational diabetes mellitus (GDM) diagnostic markers and establish the predictive model of GDM.

METHODS

We downloaded the DNA methylation data of GSE70453 and GSE102177 from the Gene Expression Omnibus database. Epigenome-wide association study (EWAS) was performed to analyze the relationship between cytosine-phosphate-guanine (CpG) methylation and GDM. And then the logistic regression models were constructed, with the β-values of CpG sites as predictor variable and the GDM occurrence as binary outcome variable. Data from GSE70453 served as training sets and data from GSE102177 served as verification sets.

RESULTS

The EWAS and overlap analysis identified nine-shared significant CpGs in the two DNA methylation data sets. Remarkably, these nine CpGs were differently methylated in GDM samples compared to their matched normal specimens, among which five fully methylated CpGs were finally selected. Importantly, we established a binary logistic regression model based on the above five CpGs, in which cg11169102, cg21179618 and cg21620107 were critical. Hence, we further built a logistic regression model by using the three CpGs and found that the area under the curve was 0.8209. The validation of the model by using the verification sets indicated the area under the curve was 0.8519.

CONCLUSIONS

We identified potential CpG biomarkers for the diagnosis of gestational diabetes mellitus patients through using EWAS and Logistic regression models in combination.

Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning

Background

Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis.

Methods

DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable.

Results

We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667.

Conclusion

We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

Immune Checkpoint Inhibitor-Induced Diabetes Mellitus: Potential Role of T Cells in the Underlying Mechanism

Immunotherapy is now a recognized treatment option for several types of cancer. However, some cancer patients treated with immune checkpoint inhibitors (ICIs) are subject to immune-related adverse events, including induced diabetes mellitus. The exact role and molecular/genetic action of ICIs in diabetes are still not well understood. Elucidating the underlying mechanisms in a proper fashion would allow better refining of biomarkers that would help diagnose patients at risk of altered immune system homeostasis, but would also hold the potential of new therapeutic options for diabetes. In the present narrative review, we propose to discuss the case of autoimmune diabetes following treatment with ICIs and the role of ICIs in the pathophysiology of diabetes. We also present some scarce available data on interesting potential immune therapies for diabetes.

Immune checkpoint molecules on T cell subsets of pregnancies with preeclampsia and gestational diabetes mellitus

Immune checkpoint molecules may play a crucial role in safeguarding pregnancy by regulating immune responses at the maternal-fetal interface. In this study, we aim to investigate the expression of PD-1, GITR, HLA-G, and CTLA-4 on T cell subsets in peripheral blood (PB), retroplacental blood (RPB), and cord blood (CB) in normal pregnancy (NP), preeclampsia (PE) and gestational diabetes mellitus (GDM). PB, RPB, and CB were collected immediately after delivery, and the expression of PD-1, GITR, HLA-G, and CTLA-4 on T cell subsets were measured by flow cytometric analysis. The proportions of Tregs in PB, RPB, and CB from NP were significantly higher than those of PE and GDM (P < 0.01, respectively). PD-1⁺ and GITR⁺ T cell subsets (CD3⁺, CD4⁺, and CD8⁺ T cells, and Tregs) in PB, as well as PD-1⁺ T cell subsets in RPB from NP, were significantly higher than those of PE and GDM (P < 0.01, respectively). In NP, PE, and GDM, the proportion of PD-1⁺ Tregs was significantly decreased in CB as compared to those of PB and RPB (P < 0.05, respectively) and the proportion of GITR⁺ Tregs was significantly higher in PB as compared to those of CB and RPB (P < 0.01, respectively). The proportion of HLA-G⁺ Tregs in PB was significantly lower than those of CB and RPB (P < 0.01, respectively). In conclusion, decreased PD-1⁺ and GITR⁺ T cell subsets and decreased proportion of Tregs in PB and RPB may play a role in chronic inflammatory immune activation of effector T cells in PE and GDM.