Immunosuppressive CD14+/HLA-DRlow/‒ monocytes in patients with Chagas Disease

Cardiac and Digestive Forms of Chagas Disease: An Update on Pathogenesis, Genetics, and Therapeutic Targets

Chagas disease, caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), is a neglected disease affecting around 6 million people, with no effective antiparasitic drugs or vaccines. About 40% of Chagas disease patients develop symptomatic forms in the chronic phase of infection, chronic Chagas cardiomyopathy (CCC) or digestive forms like megaoesophagus and megacolon, while most infected patients (60%) remain asymptomatic (ASY) in the so-called indeterminate form (IF). CCC is an inflammatory cardiomyopathy that occurs decades after the initial infection. Death results from heart failure or arrhythmia in a subset of CCC patients. Myocardial fibrosis, inflammation, and mitochondrial dysfunction are involved in heart failure and arrhythmia. Survival in CCC is worse than in other cardiomyopathies. Distinct from other cardiomyopathies, CCC displays a helper T-cell type 1 (Th1-T) cell-rich myocarditis with abundant interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) and selectively lower levels of mitochondrial energy metabolism enzymes and high-energy phosphates in the heart. A CD8+ T cell-rich inflammatory infiltrate has also been found in the Chagasic megaesophagus, which is associated with denervation of myoenteric plexi. IFN-γ and TNF-α signaling, which are constitutively upregulated in Chagas disease patients, negatively affect mitochondrial function and adenosine 5'-triphosphate (ATP) production-cytokine-induced mitochondrial dysfunction. In addition, the differential susceptibility to developing CCC has prompted many studies over the past 25 years on the association of genetic polymorphisms with disease outcomes. A comprehensive understanding of Chagas disease pathogenesis is crucial for identifying potential therapeutic targets. Genetic studies may offer valuable insights into factors with prognostic significance. In this review, we present an updated perspective on the pathogenesis and genetic factors associated with Chagas disease, emphasizing key studies that elucidate the differential progression of patients to CCC and other symptomatic forms. Furthermore, we explore the interplay between genetic susceptibility, inflammatory cytokines, mitochondrial dysfunction and discuss emerging therapeutic targets.

Prediction of Potential Drugs Targeting Acute Pancreatitis Based on the HLA-DR-Related Gene-Monocyte Infiltration Regulatory Network

Background

Acute pancreatitis (AP) is a common disease of acute abdominal pain, the incidence of which is increasing annually, but its pathogenesis remains incompletely understood.

Methods

Gene expression profiles of AP were obtained from the Gene Expression Omnibus (GEO) database. R software was used to identify differentially expressed genes (DEGs) and perform functional analysis. The diagnostic value of HLA-DR-related genes was assessed by receiver operating characteristic (ROC) curves. Monocyte infiltration abundance in AP and normal groups was analyzed by Cibersort method, and the correlation between HLA-DR-related genes and monocyte abundance was analyzed. The modules highly correlated with HLA-DR-related genes were clarified by WGCNA modeling, and the core genes regulating HLA-DR were obtained by using LASSO regression. Finally, potential drugs targeting the above genes were analyzed by Enrichr database.

Result

A Total of 3 HLA-DR-related genes (HLA-DRA, HLA-DRB1, and HLA-DRB5) were identified, which were negatively correlated with the severity of AP and had excellent disease diagnostic value (AUC = 0.761, 0.761, and 0.718), were were positively correlated with monocyte abundance. We identified 110 genes that positively regulate HLA-DR and 130 genes that negatively regulate HLA-DR. LASSO regression identified UCP2, GK, and SAMHD1 as the core nodes of the regulated genes. Compared with the normal group, UCP2 and SAMHD1 were reduced in AP, and the opposite was true for GK, and SAMHD1 had better sensitivity and specificity in diagnosing AP. Drug sensitivity analysis predicted 12 drugs acting on HLA-DRA, HLA-DRB1, and HLA-DRB5 and 8 drugs acting on UCP2, GK, and SAMHD1.

Conclusion

We identified 3 HLA-DR-related genes (HLA-DRA, HLA-DRB1, and HLA-DRB5) and 3 coregulatory nodes (UCP2, GK, and SAMHD1), which were associated with AP severity and monocyte abundance. Based on these genes, we predicted 20 potential therapeutic agents for AP.

Development and validation of machine learning-based prediction model for severe pneumonia: A multicenter cohort study

Severe pneumonia (SP) is a prevalent respiratory ailment characterized by high mortality and poor prognosis. Current scoring systems for pneumonia are not only time-consuming but also exhibit limitations in early SP prediction. To address this gap, this study aimed to develop a machine-learning model using inflammatory markers from peripheral blood for early prediction of SP. A total of 204 pneumonia patients from seven medical centers were studied, with 143 (68 SP cases) in the training cohort and 61 (32 SP cases) in the test cohort. Clinical characteristics and laboratory test results were collected at diagnosis. Various models including Logistic Regression, Random Forest, Naïve Bayes, XGBoost, Support Vector Machine, and Decision Tree were built and evaluated. Seven predictors-age, sex, WBC count, T-lymphocyte count, NLR, CRP, TNF-α, IL-4/IFN-γ ratio, IL-6/IL-10 ratio-were selected through LASSO regression and clinical insight. The XGBoost model, exhibiting best performance, achieved an AUC of 0.901 (95 % CI: 0.827 to 0.985) in the test cohort, with an accuracy of 0.803, sensitivity of 0.844, specificity of 0.759, and F1_score of 0.818. Indeed, SHAP analysis emphasized the significance of elevated WBC counts, older age, and elevated CRP as the top predictors. The use of inflammatory biomarkers in this concise predictive model shows significant potential for the rapid assessment of SP risk, thereby facilitating timely preventive interventions.

Myeloid derived suppressor cells in peripheral blood can be a prognostic factor in canine transitional cell carcinoma

Myeloid-derived suppressor cells (MDSCs) are immature cells with immunosuppressive properties found in the tumor microenvironment. MDSCs are divided into two major subsets: polymorphonuclear MDSCs (PMN-MDSCs) and monocytic MDSCs (M-MDSCs). Both MDSC subsets contribute to the creation of an immunosuppressive environment for tumor progression. In humans, patients with high levels of MDSCs show worse outcomes for several types of cancers. However, the association between MDSCs and clinical features has rarely been investigated in canine studies. In the present study, we measured the proportion of PMN-MDSCs and M-MDSCs in the peripheral blood and tumor tissue of dogs with hepatocellular carcinoma (HCC), prostate cancer (PC), transitional cell carcinoma (TCC), lymphoma, and pulmonary adenocarcinoma. Additionally, we examined immunosuppressive ability of PMN-MDSCs and M-MDSCs in peripheral blood mononuclear cells of TCC case on CD4+, CD8+ and interferon-γ+ cells and investigated the relationships of MDSCs with clinical features and outcomes. PMN-MDSCs increased in HCC, PC, TCC, and lymphoma. In contrast, M-MDSCs increased in the TCC. Both PMN-MDSCs and M-MDSCs exhibited immunosuppressive effects on CD8+, CD4+ and interferon-γ+ cells. In dogs with TCC, lymph node metastasis was associated with high level of PMN-MDSCs but not with M-MDSCs. High levels of both PMN-MDSCs and M-MDSCs were related to advanced tumor stage. Kaplan-Meier analysis revealed that high levels of both PMN-MDSCs and M-MDSCs were significantly associated with shorter overall survival. In addition, the Cox proportional hazard regression model showed that M-MDSCs and the tumor stage were independent prognostic factors for TCC. These results suggest that PMN-MDSCs and M-MDSCs may be involved in tumor progression and could be prognostic factors and promising therapeutic targets in dogs with TCC.