Enzyme-labeled Antigen Method: Development and Application of the Novel Approach for Identifying Plasma Cells Locally Producing Disease-specific Antibodies in Inflammatory Lesions

Identification of cross-talk pathways and PANoptosis-related genes in periodontitis and Alzheimer's disease by bioinformatics analysis and machine learning

Background and objectives

Periodontitis (PD), a chronic inflammatory disease, is a serious threat to oral health and is one of the risk factors for Alzheimer's disease (AD). A growing body of evidence suggests that the two diseases are closely related. However, current studies have not provided a comprehensive understanding of the common genes and common mechanisms between PD and AD. This study aimed to screen the crosstalk genes of PD and AD and the potential relationship between cross-talk and PANoptosis-related genes. The relationship between core genes and immune cells will be analyzed to provide new targets for clinical treatment.

Materials and methods

The PD and AD datasets were downloaded from the GEO database and differential expression analysis was performed to obtain DEGs. Overlapping DEGs had cross-talk genes linking PD and OP, and PANoptosis-related genes were obtained from a literature review. Pearson coefficients were used to compute cross-talk and PANoptosis-related gene correlations in the PD and AD datasets. Cross-talk genes were obtained from the intersection of PD and AD-related genes, protein-protein interaction(PPI) networks were constructed and cross-talk genes were identified using the STRING database. The intersection of cross-talk and PANoptosis-related genes was defined as cross-talk-PANoptosis genes. Core genes were screened using ROC analysis and XGBoost. PPI subnetwork, gene-biological process, and gene-pathway networks were constructed based on the core genes. In addition, immune infiltration on the PD and AD datasets was analyzed using the CIBERSORT algorithm.

Results

366 cross-talk genes were overlapping between PD DEGs and AD DEGs. The intersection of cross-talk genes with 109 PANoptosis-related genes was defined as cross-talk-PANoptosis genes. ROC and XGBoost showed that MLKL, DCN, IL1B, and IL18 were more accurate than the other cross-talk-PANoptosis genes in predicting the disease, as well as better in overall characterization. GO and KEGG analyses showed that the four core genes were involved in immunity and inflammation in the organism. Immune infiltration analysis showed that B cells naive, Plasma cells, and T cells gamma delta were significantly differentially expressed in patients with PD and AD compared with the normal group. Finally, 10 drugs associated with core genes were retrieved from the DGIDB database.

Conclusion

This study reveals the joint mechanism between PD and AD associated with PANoptosis. Analyzing the four core genes and immune cells may provide new therapeutic directions for the pathogenesis of PD combined with AD.

Immune infiltration and diagnostic value of immune-related genes in periodontitis using bioinformatics analysis

BACKGROUND AND OBJECTIVES

Periodontitis, which is a chronic inflammatory periodontal disease resulting in destroyed periodontal tissue, is the leading cause of tooth loss in adults. Many studies have found that inflammatory immune responses are involved in the risk of periodontal tissue damage. Therefore, we analyzed the association between immunity and periodontitis using bioinformatics methods to further understand this disease.

MATERIALS AND METHODS

First, the expression profiles of periodontitis and healthy samples were downloaded from the GEO database, including a training dataset GSE16134 and an external validation dataset GSE10334. Then, differentially expressed genes were identified using the limma package. Subsequently, immune cell infiltration was calculated by using the CIBERSORT algorithm. We further identified genes linking periodontitis and immunity from the ImmPort and DisGeNet databases. In addition, some of them were selected to construct a diagnostic model via a logistic stepwise regression analysis.

RESULTS AND CONCLUSIONS

Two hundred sixty differentially expressed genes were identified and found to be involved in responses to bacterial and immune-related processes. Subsequently, immune cell infiltration analysis demonstrates significant differences in the abundance of most immune cells between periodontitis and healthy samples, especially in plasma cells. These results suggested that immunity doses play a non-negligible role in periodontitis. Twenty-one genes linking periodontitis and immunity were further identified. And nine hub genes of them were identified that may be key genes involved in the development of periodontitis. Gene ontology analyses showed that these genes are involved in response to molecules of bacterial origin, cell chemotaxis, and response to chemokines. In addition, three genes of them were selected to construct a diagnostic model. And its good diagnostic performance was demonstrated by the receiver operating characteristic curves, with an area under the curve of 0.9424 for the training dataset and 0.9244 for the external validation dataset.

Differential immune cell infiltrations between healthy periodontal and chronic periodontitis tissues

Background

Host immunity plays an important role against oral microorganisms in periodontitis.

Methods

This study assessed the infiltrating immune cell subtypes in 133 healthy periodontal and 210 chronic periodontitis tissues from Gene Expression Omnibus (GEO) datasets using the CIBERSORT gene signature files.

Results

Plasma cells, naive B cells and neutrophils were all elevated in periodontitis tissues, when compared to those in healthy controls. In contrast, memory B cells, resting dendritic, mast cells and CD4 memory cells, as well as activated mast cells, M1 and M2 macrophages, and follicular helper T cells, were mainly present in healthy periodontal tissues. Furthermore, these periodontitis tissues generally contained a higher proportion of activated CD4 memory T cells, while the other subtypes of T cells, including resting CD4 memory T cells, CD8 T cells, follicular helper T cells (T_FH) and regulatory T cells (Tregs), were relatively lower in periodontitis tissues, when compared to healthy tissues. The ratio of dendritic and mast cells and macrophages was lower in periodontitis tissues, when compared to healthy tissues. In addition, there was a significant negative association of plasma cells with most of the other immune cells, such as plasma cells vs. memory B cells (γ = − 0.84), plasma cells vs. resting dendritic cells (γ = − 0.64), plasma cells vs. resting CD4 memory T cells (γ = 0.50), plasma cells versus activated dendritic cells (γ = − 0.46), plasma cells versus T_FH (γ = − 0.46), plasma cells versus macrophage M2 cells (γ = − 0.43), or plasma cells versus macrophage M1 cells (γ = − 0.40), between healthy control and periodontitis tissues.

Conclusion

Plasma cells, naive B cells and neutrophils were all elevated in periodontitis tissues. The infiltration of different immune cell subtypes in the periodontitis site could lead the host immunity against periodontitis.

Associations between Viral Infection History Symptoms, Granulocyte Reactive Oxygen Species Activity, and Active Rheumatoid Arthritis Disease in Untreated Women at Onset: Results from a Longitudinal Cohort Study of Tatarstan Women

To evaluate the effects of infectious episodes at early stages of rheumatoid arthritis (eRA) development, 59 untreated eRA patients, 77 first-degree relatives, from a longitudinal Tatarstan women cohort, were included, and compared to 67 healthy women without rheumatoid arthritis (RA) in their family history. At inclusion, informations were collected regarding both the type and incidence of infectious symptom episodes in the preceding year, and granulocyte reactive oxygen species (ROS) were studied at the basal level and after stimulation with serum-treated zymosan (STZ). In the eRA group, clinical [disease activity score (DAS28), health assessment questionnaire] and biological parameters associated with inflammation (erythrocyte sedimentation rate, C-reactive protein) or with RA [rheumatoid factor, anticyclic citrullinated peptide (anti-CCP2) antibodies] were evaluated. An elevated incidence of infection events in the previous year characterized the eRA and relative groups. In addition, a history of herpes simplex virus (HSV) episodes was associated with disease activity, while an elevated incidence of anti-CCP2 autoantibody characterized eRA patients with a history of viral upper respiratory tract infection symptoms (V-URI). Granulocyte ROS activity in eRA patients was quantitatively [STZ peak and its area under the curve (AUC)] and qualitatively (STZ time of peak) altered, positively correlated with disease activity, and parameters were associated with viral symptoms including HSV exacerbation/recurrence, and V-URI. In conclusion, our study provides arguments to consider a history of increased viral infection symptoms in RA at the early stage and such involvement needs to be studied further.