Upregulated Vanins and their potential contribution to periodontitis

Differential expression of FSTL1 and its correlation with the pathological process of periodontitis

AIMS

This study aimed to elucidate the alterations in Follistatin-like protein 1 (FSTL1) and its association with the pathological process of periodontitis.

METHODS

This study included 48 patients with periodontitis and 42 healthy controls. The expression level of FSTL1 in the gingiva was determined by RT-qPCR, validated using the dataset GSE16134, and subsequently examined by western blotting. Bioinformatics analysis revealed a single-cell distribution of FSTL1, characteristic of angiogenesis and immune cell infiltration. The expression and distribution of FSTL1, vascular endothelial marker protein CD31 and myeloperoxidase (MPO), the indicator of neutrophil activity, were determined by immunohistochemistry (IHC). A series of correlation analyses was performed to determine the associations between FSTL1 and clinical parameters, including probing depth (PD) and clinical attachment loss (CAL), and their potential role in angiogenesis (CD31) and neutrophil infiltration (MPO).

RESULTS

FSTL1 was significantly upregulated in the gingiva of patients with periodontitis compared to their healthy counterparts. In addition, FSTL1 was positively correlated with the clinical parameters PD (r = .5971, p = .0005) and CAL (r = .6078, p = .0004). Bioinformatic analysis and IHC indicated that high FSTL1 expression was significantly correlated with angiogenesis and neutrophil infiltration in periodontitis. Moreover, receiver operating characteristic (ROC) analysis demonstrated that FSTL1 could serve as an independent indicator for evaluating the severity of periodontitis (area under the curve [AUC] = 0.9011, p < .0001).

CONCLUSION

This study demonstrated FSTL1 upregulation in periodontitis and its potential contribution to the disease via angiogenesis and neutrophil infiltration.

OralExplorer: a web server for exploring the mechanisms of oral inflammatory diseases

BACKGROUND

Oral inflammatory diseases are localized infectious diseases primarily caused by oral pathogens with the potential for serious systemic complications. However, publicly available datasets for these diseases are underutilized. To address this issue, a web tool called OralExplorer was developed. This tool integrates the available data and provides comprehensive online bioinformatic analysis.

METHODS

Human oral inflammatory disease-related datasets were obtained from the GEO database and normalized using a standardized process. Transcriptome data were then subjected to differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis, and visualization. The single-cell sequencing data was visualized as cluster plot, feature plot, and heatmaps. The web platform was primarily built using Shiny. The biomarkers identified in OralExplorer were validated using local clinical samples through qPCR and IHC.

RESULTS

A total of 35 human oral inflammatory disease-related datasets, covering 6 main disease types and 901 samples, were included in the study to identify potential molecular signatures of the mechanisms of oral diseases. OralExplorer consists of 5 main analysis modules (differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis and single-cell analysis), with multiple visualization options. The platform offers a simple and intuitive interface, high-quality images for visualization, and detailed analysis results tables for easy access by users. Six markers (IL1β, SRGN, CXCR1, FGR, ARHGEF2, and PTAFR) were identified by OralExplorer. qPCR- and IHC-based experimental validation showed significantly higher levels of these genes in the periodontitis group.

CONCLUSIONS

OralExplorer is a comprehensive analytical platform for oral inflammatory diseases. It allows users to interactively explore the molecular mechanisms underlying the action and regression of these diseases. It also aids dental researchers in unlocking the potential value of transcriptomics data related to oral diseases. OralExplorer can be accessed at https://smuonco.shinyapps.io/OralExplorer/  (Alternate URL: http://robinl-lab.com/OralExplorer ).

Vanin1 (VNN1) in chronic diseases: Future directions for targeted therapy

Vanin1 (VNN1) is an exogenous enzyme with pantetheinase activity that mainly exerts physiological functions through enzyme catalysis products, including pantothenic acid and cysteamine. In recent years, the crosstalk between VNN1 and metabolism and oxidative stress has attracted much attention. As a result of the ability of VNN1 to affect multiple metabolic pathways and oxidative stress to exacerbate or alleviate pathological processes, it has become a key component of disease progression. This review discusses the functions of VNN1 in glucolipid metabolism, cysteamine metabolism, and glutathione metabolism to provide perspectives on VNN1-targeted therapy for chronic diseases.

Significant alteration of protein profiles in a mouse model of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is an endocrine disorder, affecting women of child-bearing age, and the incidence rate is growing and assuming epidemic proportions. The etiology of PCOS remains unknown and there is no cure. Some animal models for PCOS have been established which have enhanced our understanding of the underlying mechanisms, but omics data for revealing PCOS pathogenesis and for drug discovery are still lacking. In the present study, proteomics analysis was used to construct a protein profile of the ovaries in a PCOS mouse model. The result showed a clear difference in protein profile between the PCOS and control group, with 495 upregulated proteins and 404 downregulated proteins in the PCOS group. The GO term and KEGG pathway analyses of differentially expressed proteins mainly showed involvement in lipid metabolism, oxidative stress, and immune response, which are consistent with pathological characteristics of PCOS in terms of abnormal metabolism, endocrine disorders, chronic inflammation and imbalance between oxidant and antioxidant levels. Also, we found that inflammatory responses were activated in the PCOS ovarium, while lipid biosynthetic process peroxisome, and bile secretion were inhibited. In addition, we found some alteration in unexpected pathways, such as glyoxylate and dicarboxylate metabolism, which should be investigated. The present study makes an important contribution to the current lack of PCOS ovarian proteomic data and provides an important reference for research and development of effective drugs and treatments for PCOS.

Identification and assessment of differentially expressed necroptosis long non-coding RNAs associated with periodontitis in human

BACKGROUND

Periodontitis is the most common oral disease and is closely related to immune infiltration in the periodontal microenvironment and its poor prognosis is related to the complex immune response. The progression of periodontitis is closely related to necroptosis, but there is still no systematic study of long non-coding RNA (lncRNA) associated with necroptosis for diagnosis and treatment of periodontitis.

MATERIAL AND METHODS

Transcriptome data and clinical data of periodontitis and healthy populations were obtained from the Gene Expression Omnibus (GEO) database, and necroptosis-related genes were obtained from previously published literature. FactoMineR package in R was used to perform principal component analysis (PCA) for obtaining the necroptosis-related lncRNAs. The core necroptosis-related lncRNAs were screened by the Linear Models for Microarray Data (limma) package in R, PCA principal component analysis and lasso algorithm. These lncRNAs were then used to construct a classifier for periodontitis with logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the model. The CIBERSORT method and ssGSEA algorithm were used to estimate the immune infiltration and immune pathway activation of periodontitis. Spearman's correlation analysis was used to further verify the correlation between core genes and periodontitis immune microenvironment. The expression level of core genes in human periodontal ligament cells (hPDLCs) was detected by RT-qPCR.

RESULTS

A total of 10 core necroptosis-related lncRNAs (10-lncRNAs) were identified, including EPB41L4A-AS1, FAM30A, LINC01004, MALAT1, MIAT, OSER1-DT, PCOLCE-AS1, RNF144A-AS1, CARMN, and LINC00582. The classifier for periodontitis was successfully constructed. The Area Under the Curve (AUC) was 0.952, which suggested that the model had good predictive performance. The correlation analysis of 10-lncRNAs and periodontitis immune microenvironment showed that 10-lncRNAs had an impact on the immune infiltration of periodontitis. Notably, the RT-qPCR results showed that the expression level of the 10-lncRNAs obtained was consistent with the chip analysis results.

CONCLUSIONS

The 10-lncRNAs identified from the GEO dataset had a significant impact on the immune infiltration of periodontitis and the classifier based on 10-lncRNAs had good detection efficiency for periodontitis, which provided a new target for diagnosis and treatment of periodontitis.