Tim-3 Expression Causes NK Cell Dysfunction in Type 2 Diabetes Patients

Interaction of the Immune System TIM-3 Protein with a Model Cellular Membrane Containing Phosphatidyl-Serine Lipids

T cell transmembrane, Immunoglobulin, and Mucin (TIM) are important immune system proteins which are especially present in T-cells and regulated the immune system by sensing cell engulfment and apoptotic processes. Their role is exerted by the capacity to detect the presence of phosphatidyl-serine lipid polar head in the outer leaflet of cellular membranes (correlated with apoptosis). In this contribution by using equilibrium and enhanced sampling molecular dynamics simulation we unravel the molecular bases and the thermodynamics of TIM, and in particular TIM-3, interaction with phosphatidyl serine in a lipid bilayer. Since TIM-3 deregulation is an important factor of pro-oncogenic tumor micro-environment understanding its functioning at a molecular level may pave the way to the development of original immunotherapeutic approaches.

Research Progress on the Weak Immune Response to the COVID-19 Vaccine in Patients with Type 2 Diabetes

Coronavirus Disease 2019 (COVID-19) is generally susceptible to the population, highly infectious, rapidly transmitted, and highly fatal. There is a lack of specific drugs against the virus at present and vaccination is the most effective strategy to prevent infection. However, studies have found that some groups, particularly patients with diabetes, show varying degrees of weak immune reactivity to various COVID-19 vaccines, resulting in poor preventive efficacy against the novel coronavirus in patients with diabetes. Therefore, in this study, patients with type 2 diabetes mellitus (T2DM) who had weak immune response to the COVID-19 vaccine in recent years were analyzed. This article reviews the phenomenon, preliminary mechanism, and related factors affecting weak vaccine response in patients with T2DM, which is expected to help in the development of new vaccines for high-risk groups for COVID-19.

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.

Immunological link between periodontitis and type 2 diabetes deciphered by single-cell RNA analysis

BACKGROUND

Type 2 diabetes mellitus (DM) is a complex metabolic disorder that causes various complications, including periodontitis (PD). Although a bidirectional relationship has been reported between DM and PD, their immunological relationship remains poorly understood. Therefore, this study aimed to compare the immune response in patients with PD alone and in those with both PD and DM (PDDM) to expand our knowledge of the complicated connection between PD and DM.

METHODS

Peripheral blood mononuclear cells were collected from 11 healthy controls, 10 patients with PD without DM, and six patients with PDDM, followed by analysis using single-cell RNA sequencing. The differences among groups were then compared based on intracellular and intercellular perspectives.

RESULTS

Compared to the healthy state, classical monocytes exhibited the highest degree of transcriptional change, with elevated levels of pro-inflammatory cytokines in both PD and PDDM. DM diminished the effector function of CD8+ T and natural killer (NK) cells as well as completely modified the differentiation direction of these cells. Interestingly, a prominent pathway, RESISTIN, which is known to increase insulin resistance and susceptibility to diabetes, was found to be activated under both PD and PDDM conditions. In particular, CAP1+ classical monocytes from patients with PD and PDDM showed elevated nuclear factor kappa B-inducing kinase activity.

CONCLUSIONS

Overall, this study elucidates how the presence of DM contributes to the deterioration of T/NK cell immunity and the immunological basis connecting PD to DM.

Dysfunction of circulating CD3+CD56+ NKT-like cells in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is associated with increased incidence and mortality of many cancers and infectious diseases. CD3⁺CD56⁺ NKT-like cells play pivotal roles in tumor surveillance and infection control. However, little is known about potential alterations in circulating NKT-like cells in T2DM patients. In this study, we found that the frequency and absolute counts of circulating NKT-like cells were significantly lower in patients with T2DM compared to healthy volunteers. Moreover, in T2DM patients, NKT-like cells were impaired in their production of IFN-γ and TNF-α as well as degranulation capacity. The expression of activating receptor NKG2D was markedly decreased on NKT-like cells in T2DM patients, while the expression of inhibitory receptors Tim-3 and LAG-3 was upregulated. In detail, Tim-3⁺NKT-like cells expressed higher LAG-3 and less IFN-γ and TNF-α compared to Tim-3^-NKT-like cells. Importantly, we further found that the expression of Tim-3 in NKT-like cells from T2DM patients correlated positively with glycated hemoglobin (HbA1c) and fasting blood glucose (FBG) levels, as well as with diabetes duration. In conclusion, these results indicate that NKT-like cells from T2DM patients display an exhausted phenotype and reduced functionality. Moreover, Tim-3 expression on NKT-like cells likely serves a novel biomarker for duration of T2DM.

Metabolism of NK cells during viral infections

Cellular metabolism is essential for the correct function of immune system cells, including Natural Killer cells (NK). These cells depend on energy to carry out their effector functions, especially in the early stages of viral infection. NK cells participate in the innate immune response against viruses and tumors. Their main functions are cytotoxicity and cytokine production. Metabolic changes can impact intracellular signals, molecule production, secretion, and cell activation which is essential as the first line of immune defense. Metabolic variations in different immune cells in response to a tumor or pathogen infection have been described; however, little is known about NK cell metabolism in the context of viral infection. This review summarizes the activation-specific metabolic changes in NK cells, the immunometabolism of NK cells during early, late, and chronic antiviral responses, and the metabolic alterations in NK cells in SARS-CoV2 infection. The modulation points of these metabolic routes are also discussed to explore potential new immunotherapies against viral infections.