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. 2023 Jan 6:13:1098851.
doi: 10.3389/fphar.2022.1098851. eCollection 2022.

Identification of ferroptosis, necroptosis, and pyroptosis-associated genes in periodontitis-affected human periodontal tissue using integrated bioinformatic analysis

Shuaifei Pan  1 , Yue Li  1 , Haokun He  1 , Shuguang Cheng  1 , Jiang Li  1 , Janak L Pathak  1
Affiliations

Identification of ferroptosis, necroptosis, and pyroptosis-associated genes in periodontitis-affected human periodontal tissue using integrated bioinformatic analysis

Shuaifei Pan et al. Front Pharmacol. .

Abstract

Introduction: Periodontitis is a chronic inflammatory oral disease that destroys soft and hard periodontal support tissues. Multiple cell death modes including apoptosis, necroptosis, pyroptosis, and ferroptosis play a crucial role in the pathogenicity of inflammatory diseases. This study aimed to identify genes associated with ferroptosis, necroptosis, and pyroptosis in different cells present in the periodontium of periodontitis patients. Methods: Gingival tissues' mRNA sequencing dataset GSE173078 of 12 healthy control and 12 periodontitis patients' and the microarray dataset GSE10334 of 63 healthy controls and 64 periodontitis patients' were obtained from Gene Expression Omnibus (GEO) database. A total of 910 differentially expressed genes (DEGs) obtained in GSE173078 were intersected with necroptosis, pyroptosis, and ferroptosis-related genes to obtain the differential genes associated with cell death (DCDEGs), and the expression levels of 21 differential genes associated with cell death were verified with dataset GSE10334. Results: Bioinformatic analysis revealed 21 differential genes associated with cell death attributed to ferroptosis, pyroptosis, and necroptosis in periodontitis patients compared with healthy controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that 21 differential genes associated with cell death were related to various cellular and immunological pathways including inflammatory responses, necroptosis, and osteoclast differentiation. Additionally, the single-cell RNA (scRNA) sequencing data GSE171213 of 4 healthy controls and 5 periodontitis patients' periodontal tissue was analyzed to obtain cell clustering and cell types attributed to differential genes associated with cell death. We found that among 21 DCDEGs, SLC2A3, FPR2, TREM1, and IL1B were mainly upregulated in neutrophils present in the periodontium of periodontitis patients. Gene overlapping analysis revealed that IL-1B is related to necroptosis and pyroptosis, TREM1 and FPR2 are related to pyroptosis, and SLC2A3 is related to ferroptosis. Finally, we utilized the CIBERSORT algorithm to assess the association between DCDEGs and immune infiltration phenotypes, based on the gene expression profile of GSE10334. The results revealed that the upregulated SLC2A3, FPR2, TREM1, and IL1B were positively correlated with neutrophil infiltration in the periodontium. Discussion: The findings provide upregulated SLC2A3, FPR2, TREM1, and IL1B in neutrophils as a future research direction on the mode and mechanism of cell death in periodontitis and their role in disease pathogenicity.

Keywords: bioinformatic analysis; ferroptosis; necroptosis; neutrophils; periodontitis; pyroptosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flowchart of the multistep screening strategy on bioinformatics data, differentially expressed genes (DEGs), differentially expressed genes; differentially expressed cell death-related genes (DCDEGs), differential genes associated with cell death; scRNA, single-cell transcriptomic; PPI, protein-protein interaction.
FIGURE 2
FIGURE 2
Screening of the differentially expressed cell death-related genes (DCDEGs) in periodontitis. (A) The volcano of differentially expressed genes (DEGs) in GSE173078. (periodontitis group vs. control group) (B) Venn diagram showing the overlap of genes between DEGs in GSE173078 and cell death-related genes.
FIGURE 3
FIGURE 3
(A) Clustered heatmap of DCDEGs. (B) The bar graph shows the mRNA expression of 21 DCDEGs in GSE173078. Wilcoxon rank-sum test was used to calculate the p-value. Significant difference between the groups, *p < .05, **p < .01, and ***p < .001.
FIGURE 4
FIGURE 4
GO and KEGG enrichment analysis of DCDEGs in GSE173078. (A) Enriched items in GO analysis. (B) Enriched pathways in KEGG analysis. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes. The top 10 pathways based on p.adjust-value ranking are listed.
FIGURE 5
FIGURE 5
Correlation alanysis of 21 DCDGEs. (A) The PPI network of DCDEGs based on STRING. IL1B serves as a hub gene. (B) Pearson’s correlation analysis.
FIGURE 6
FIGURE 6
The 21 DCDEGs were validated in the GSE10334 dataset. Wilcoxon rank-sum test was used to calculate the p-value. Significant difference, *p < .05, **p < .01, and ***p < .001.
FIGURE 7
FIGURE 7
scRNA analysis in periodontitis. (A) UMAP projection of 34,417 cells in periodontitis. (B) The proportion of each cell cluster in the different sample sets, HC, healthy control; PD, periodontitis.
FIGURE 8
FIGURE 8
(A) Dotplot of DCDEGs. (B) The volcano plot of DCDEGs in the neutrophil cluster.
FIGURE 9
FIGURE 9
Proportion of 22 types of immune cells infiltrated in gingival tissue of periodontitis patients.
FIGURE 10
FIGURE 10
Wilcoxon rank-sum test and spearman correlation were used to explore the correlation between DCDEGs and immune cells by immune infiltration analysis. Significant difference, *p < .05, **p < .01, and ***p < .001.
See this image and copyright information in PMC

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