Dataset Information

The underlying molecular mechanisms and biomarkers between periodontitis and COVID-19.

ABSTRACT:

Objective

Emerging evidence shows the clinical consequences of patient with COVID-19 and periodontitis are not promising, and periodontitis is a risk factor. Periodontitis and COVID-19 probably have a relationship. Hence, this study aimed to identify the common molecular mechanism that may help to devise potential therapeutic strategies in the future.

Material and methods

We analyzed two RNA-seq datasets for differential expressed genes, enrichment of biological processes, transcription factors (TFs) and deconvolution-based immune cell types in periodontitis, COVID-19 and healthy controls. Relationships between TFs and mRNA were established by Pearson correlation analysis, and the common TFs-mRNA regulatory network and nine co-upregulated TFs of the two diseases was obtained. The RT-PCR detected the TFs.

Results

A total of 1616 and 10201 differentially expressed gene (DEGs) from periodontitis and COVID-19 are found. Moreover, nine shared TFs and common biological processes associated with lymphocyte activation involved in immune response were identified across periodontitis and COVID-19. The cell type enrichment revealed elevated plasma cells among two diseases. The RT-PCR further confirmed the nine TFs up-regulation in periodontitis.

Conclusion

The pathogenesis of periodontitis and COVID-19 is closely related to the expression of TFs and lymphocyte activation, which can provide potential targets for treatment.

SUBMITTER: Qin D

PROVIDER: S-EPMC10369766 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The underlying molecular mechanisms and biomarkers between periodontitis and COVID-19.

Qin Danlei D Yu Feiyan F Wu Dongchao D Han Chong C Yao Xuemin X Yang Lulu L Yang Xi X Wang Qianqian Q He Dongning D Zhao Bin B

BMC oral health 20230726 1

<h4>Objective</h4>Emerging evidence shows the clinical consequences of patient with COVID-19 and periodontitis are not promising, and periodontitis is a risk factor. Periodontitis and COVID-19 probably have a relationship. Hence, this study aimed to identify the common molecular mechanism that may help to devise potential therapeutic strategies in the future.<h4>Material and methods</h4>We analyzed two RNA-seq datasets for differential expressed genes, enrichment of biological processes, transcr ...[more]

PMID: 37495990

Similar Datasets

Project description:BackgroundThe coronavirus disease 2019 (COVID-19), a pandemic contributing to more than 105 million cases and more than 2.3 million deaths worldwide, was described to be frequently accompanied by extrapulmonary manifestations, including liver dysfunction. Liver dysfunction and elevated liver enzymes were observed in about 53% of COVID-19 patients.AimTo gain insight into transcriptional abnormalities in liver tissue of severe COVID-19 patients that may result in liver dysfunction.MethodsThe transcriptome of liver autopsy samples from severe COVID-19 patients against those of non-COVID donors was analyzed. Differentially expressed genes were identified from normalized RNA-seq data and analyzed for the enrichment of functional clusters and pathways. The differentially expressed genes were then compared against the genetic signatures of liver diseases including cirrhosis, fibrosis, non-alcoholic fatty liver disease (NAFLD), and hepatitis A/B/C. Gene expression of some differentially expressed genes was assessed in the blood samples of severe COVID-19 patients with liver dysfunction using qRT-PCR.ResultsAnalysis of the differential transcriptome of the liver tissue of severe COVID-19 patients revealed a significant upregulation of transcripts implicated in tissue remodeling including G-coupled protein receptors family genes, DNAJB1, IGF2, EGFR, and HDGF. Concordantly, the differential transcriptome of severe COVID-19 liver tissues substantially overlapped with the disease signature of liver diseases characterized with pathological tissue remodeling (liver cirrhosis, Fibrosis, NAFLD, and hepatitis A/B/C). Moreover, we observed a significant suppression of transcripts implicated in metabolic pathways as well as mitochondrial function, including cytochrome P450 family members, ACAD11, CIDEB, GNMT, and GPAM. Consequently, drug and xenobiotics metabolism pathways are significantly suppressed suggesting a decrease in liver detoxification capacity. In correspondence with the RNA-seq data analysis, we observed a significant upregulation of DNAJB1 and HSP90AB1 as well as significant downregulation of CYP39A1 in the blood plasma of severe COVID-19 patients with liver dysfunction.ConclusionSevere COVID-19 patients appear to experience significant transcriptional shift that may ensue tissue remodeling, mitochondrial dysfunction and lower hepatic detoxification resulting in the clinically observed liver dysfunction.

Project description:BackgroundEndometriosis (EMs) is a common condition that causes dysmenorrhea, chronic pelvic pain, and infertility, affecting millions of women worldwide. Despite the use of assisted reproductive technology, EMs patients often experience lower embryo implantation rates and recurrent implantation failure (RIF) due to impaired uterine endometrial receptivity. This study aims to identify shared diagnostic genes and underlying mechanisms between EMs and RIF using integrated transcriptomic analysis and machine learning with Gene Expression Omnibus (GEO) datasets.MethodsWe analyzed GSE11691, GSE7305, GSE111974, and GSE103465 as training datasets for EMs and RIF, and GSE25628 and GSE92324 as validation datasets. Differentially expressed genes (DEGs) and Weighted Gene Co-Expression Network Analysis (WGCNA) identified key genes specific to and shared by EMs and RIF. Machine learning algorithms were used to determine the shared diagnostic gene, whose performance was validated in both training and validation datasets. Single-gene Gene Set Enrichment Analysis (GSEA) revealed shared biological processes in EMs and RIF, while CIBERSORT analysis highlighted similarities and differences in immune infiltration between the two conditions. Finally, endometrial samples from healthy controls, EMs, and RIF patients were collected, and qRT-PCR was performed to validate the diagnostic gene.ResultsWe identified 48 shared key genes between EMs and RIF. The diagnostic gene EHF was selected through machine learning algorithms, and its diagnostic performance was validated in both training and validation datasets. ROC curve analysis demonstrated excellent diagnostic accuracy of EHF for both diseases. Gene Set Enrichment Analysis (GSEA) revealed that both conditions shared biological processes, including dysregulated extracellular matrix remodeling and abnormal immune infiltration. Furthermore, we validated the expression of EHF in endometrial samples from healthy controls, EMs, and RIF patients. Additionally, we characterized the immune microenvironment in EMs and RIF, highlighting changes in immune cell components associated with EHF.DiscussionThe diagnostic gene EHF identified in this study may serve as a key link between EMs and RIF. The shared pathological processes in both conditions involve alterations in the extracellular matrix and subsequent changes in the immune microenvironment. These findings provide novel insights into potential therapeutic strategies for improving infertility treatment in patients with EMs.

Dataset Information

The underlying molecular mechanisms and biomarkers between periodontitis and COVID-19.

Objective

Material and methods

Results

Conclusion

Publications

The underlying molecular mechanisms and biomarkers between periodontitis and COVID-19.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets