An analysis of POMC gene methylation and expression in patients with schizophrenia

Schizophrenia is a chronic mental disorder that affects millions of people and is believed to be caused by both environmental and genetic factors. Despite extensive research, the exact mechanisms underlying schizophrenia are still unclear. Studies have shown that numerous psychiatric disorders are associated with methylation of the POMC gene, which encodes adrenocorticotropic hormone, a critical player in the hypothalamic-pituitary-adrenal axis. However, the association between DNA methylation in POMC patients and schizophrenia remains unclear. In this study, we evaluated three fragments of the POMC promoter region, including 51 CpG sites, in the peripheral blood of schizophrenia patients and healthy controls. The POMC protein level was measured via enzyme-linked immunosorbent assay (ELISA). The schizophrenia group exhibited significantly greater levels of methylation of the POMC gene than those in the control group. The methylation level of the POMC-2 fragment was significantly greater in the patient group than in the control group. There were 17 significantly hypermethylated CpG sites in the patient group. After stratification by sex, POMC methylation levels were found to be significantly greater in male schizophrenia patients than in healthy controls; the methylation levels of POMC-2 fragments were greater in the male patient group; nine CpG sites were significantly hypermethylated in the male patient group; and only one CpG site was significantly hypermethylated in the female patient group. The POMC protein level in patients was significantly lower than that in healthy controls. These findings demonstrate that the DNA methylation of POMC might be associated with the pathophysiology of schizophrenia. Overall, studying the correlation between POMC methylation and schizophrenia may contribute to the diagnosis and evaluation of neuropsychiatric disorders.

Gastrodin Regulates PI3K/AKT-Sirt3 Signaling Pathway and Proinflammatory Mediators in Activated Microglia

Activated microglia and their mediated inflammatory responses play an important role in the pathogenesis of hypoxic-ischemic brain damage (HIBD). Therefore, regulating microglia activation is considered a potential therapeutic strategy. The neuroprotective effects of gastrodin were evaluated in HIBD model mice, and in oxygen glucose deprivation (OGD)-treated and lipopolysaccharide (LPS)activated BV-2 microglia cells. The potential molecular mechanism was investigated using western blotting, immunofluorescence labeling, quantitative realtime reverse transcriptase polymerase chain reaction, and flow cytometry. Herein, we found that PI3K/AKT signaling can regulate Sirt3 in activated microglia, but not reciprocally. And gastrodin exerts anti-inflammatory and antiapoptotic effects through the PI3K/AKT-Sirt3 signaling pathway. In addition, gastrodin could promote FOXO3a phosphorylation, and inhibit ROS production in LPSactivated BV-2 microglia. Moreover, the level P-FOXO3a decreased significantly in Sirt3-siRNA group. However, there was no significant change after gastrodin and siRNA combination treatment. Notably, gastrodin might also affect the production of ROS in activated microglia by regulating the level of P-FOXO3a via Sirt3. Together, this study highlighted the neuroprotective role of PI3K/AKT-Sirt3 axis in HIBD, and the anti-inflammatory, anti-apoptotic, and anti-oxidative stress effects of gastrodin on HIBD.

Identification of molecular subtypes and a prognostic signature based on m6A/m5C/m1A-related genes in lung adenocarcinoma

Lung cancer, specifically the histological subtype lung adenocarcinoma (LUAD), has the highest global occurrence and fatality rate. Extensive research has indicated that RNA alterations encompassing m6A, m5C, and m1A contribute actively to tumorigenesis, drug resistance, and immunotherapy responses in LUAD. Nevertheless, the absence of a dependable predictive model based on m6A/m5C/m1A-associated genes hinders accurately predicting the prognosis of patients diagnosed with LUAD. In this study, we collected patient data from The Cancer Genome Atlas (TCGA) and identified genes related to m6A/m5C/m1A modifications using the GeneCards database. The "ConsensusClusterPlus" R package was used to produce molecular subtypes by utilizing genes relevant to m6A/m5C/m1A identified through differential expression and univariate Cox analyses. An independent prognostic factor was identified by constructing a prognostic signature comprising six genes (SNHG12, PABPC1, IGF2BP1, FOXM1, CBFA2T3, and CASC8). Poor overall survival and elevated expression of human leukocyte antigens and immune checkpoints were correlated with higher risk scores. We examined the associations between the sets of genes regulated by m6A/m5C/m1A and the risk model, as well as the immune cell infiltration, using algorithms such as ESTIMATE, CIBERSORT, TIMER, ssGSEA, and exclusion (TIDE). Moreover, we compared tumor stemness indices (TSIs) by considering the molecular subtypes related to m6A/m5C/m1A and risk signatures. Analyses were performed based on the risk signature, including stratification, somatic mutation analysis, nomogram construction, chemotherapeutic response prediction, and small-molecule drug prediction. In summary, we developed a prognostic signature consisting of six genes that have the potential for prognostication in patients with LUAD and the design of personalized treatments that could provide new versions of personalized management for these patients.

Comprehensive Analysis of N6-Methyladenosine RNA Methylation Regulators in the Diagnosis and Subtype Classification of Rheumatoid Arthritis

m6A modification is the most abundant mRNA modifications and plays an integral role in various biological processes in eukaryotes. However, the role of m6A regulators in rheumatoid arthritis remains unknown. To determine the expression of m6A RNA methylation regulators in rheumatoid arthritis and their possible functional and prognostic value. In this study, we performed differential analysis in the comprehensive gene expression database GSE93272 dataset between non-rheumatoid arthritis patients and rheumatoid arthritis patients to obtain 15 important m6A regulators. A random forest model and lasso regression were used to screen the five most important m6A regulators to predict the risk of developing rheumatoid arthritis. After further validation using in vitro qPCR experiments, a nomogram model was developed based on the four most important m6A regulators (ELAVL1, WTAP, YTHDF1, and ALKBH5). Immuno-infiltration analysis and consensus clustering analysis were then performed. An analysis of the decision curve showed that the nomogram model could be beneficial to patients. According to selected important m6A regulators, patients with rheumatoid arthritis were classified into two m6A models (ClusterA and ClusterB) via consensus approach. Activated B cells, CD56dim natural killer cells, immature B cells, monocytes, natural killer T cells, and T lymphocytes were associated with ClusterA in immune infiltration analysis. Importantly, immune infiltration in patients with high ELAVL1 expression was strikingly similar to ClusterA. m6A regulators play a non-negligible role in the development of rheumatoid arthritis. A study of m6A patterns may provide future therapeutic options for rheumatoid arthritis.

Analysis of characteristics related to interstitial lung disease or pulmonary hypertension in patients with dermatomyositis

INTRODUCTION

Dermatomyositis (DM) is often associated with interstitial lung disease (ILD) or pulmonary hypertension (PH). The aim of this study was to investigate the clinical characteristics of DM patients with ILD or PH.

METHODS

This study retrospectively analysed the clinical characteristics of 372 patients with DM, including cytokines, lymphocyte subsets, immunoglobulin and complement. The DM patients were divided into different groups according to whether complicated with ILD, PH or anti-melanoma differentiation-associated gene 5 antibodies (MDA5). A qualitative and quantitative data analysis was performed.

RESULTS

IgG, IgA and IgM in the DM-associated ILD (ILD-DM) were higher than that of the DM non-complicating ILD (Non-ILD-DM) (p = 0.022, 0.002 and 0.029, respectively). Meanwhile, IL-6 (p = 0.008) and IL-10 (p = 0.001) were increased in the DM-associated PH (PH-DM) than in the DM non-complicating PH (Non-PH-DM), while IL-17 (p = 0.004), double positive (DP) cell ratio and B lymphocyte ratio were reduced in the PH-DM. Moreover, the incidence of ILD and levels of C4 were higher in the DM with MDA5 (MDA5+ DM) than that of the DM without MDA5.

CONCLUSION

ILD-DM has higher IgG, IgA and IgM than that of Non-ILD-DM. PH-DM has higher IL-6, IL-10 and lower IL-17, DP cell ratio and B lymphocyte ratio than that of Non-PH-DM.

Effect of Wnt10a/β-catenin signaling pathway on promoting the repair of different types of dentin-pulp injury

How to repair dentin-pulp injury effectively has always been a clinical problem, and the comparative study of repair process between different injuries is unknown. Dental pulp stem cells (DPSCs) often are selected as seed cells for the study of dentin-pulp injury repair due to excellent advantages in odontogenesis and pulp differentiation. Although many previous researches have indicated that the Wnt protein and Wnt/β-catenin signaling pathway were crucial for dental growth, development, and injury repair, the specific mechanism remained unknown. In this study, different dentine-pulp injury models of adult mice were established successfully by abrasion and cutting methods. The gross morphology and micro-CT were used to observe the repair of injured mice incisor in different groups. We found that the repair time of each group was different. The repair time of the cutting group was longer than the abrasion group and the qRT-PCR detection showed that the expression of DSPP in the cutting group was higher than that in the abrasion group, but there was no significant difference in proliferation among the groups. In vivo and cell experiments showed that activation of Wnt/β-catenin signaling pathway can promote the proliferation and odontoblast differentiation of DPSCs. In addition, by using RNAscope staining, we observed that Wnt10a was mainly expressed in the proliferative region and partially expressed in the odontoblast region. The Western blotting results showed that in the early stage of repair, the expression of Wnt10a increased with the extension of days after injury in both abrasion and cutting group and the increase of Wnt10a was tested obviously on the 5th day after injury. But on the 7th day after injury, the expression of Wnt10a was still obvious in the cutting group, while the expression of Wnt10a was significantly reduced in the abrasion group, which was close to the control group. It is suggested that Wnt10a acts as a repair-related protein and has an important role in tooth injury repair. Wnt10a was activated by R-spondin and LiCl, and Wnt10a-siRNA DPSCs were constructed to inhibit Wnt10a. The results showed that Wnt10a/β-catenin signaling pathway promoted the proliferation and odontoblast differentiation of DPSCs. It plays a crucial role in the repair process of different injuries. This study enriched the mechanisms of Wnt10a /β-catenin signaling pathways in different types of dentin-pulp injury repair, which could provide experimental evidences for the target gene screening and also give some new ideas for the subsequent research on the molecular mechanisms of tooth regeneration.

The Structure Entropy-Based Node Importance Ranking Method for Graph Data

Due to its wide application across many disciplines, how to make an efficient ranking for nodes in graph data has become an urgent topic. It is well-known that most classical methods only consider the local structure information of nodes, but ignore the global structure information of graph data. In order to further explore the influence of structure information on node importance, this paper designs a structure entropy-based node importance ranking method. Firstly, the target node and its associated edges are removed from the initial graph data. Next, the structure entropy of graph data can be constructed by considering the local and global structure information at the same time, in which case all nodes can be ranked. The effectiveness of the proposed method was tested by comparing it with five benchmark methods. The experimental results show that the structure entropy-based node importance ranking method performs well on eight real-world datasets.