Identification of hub genes related to immune cell infiltration in periodontitis using integrated bioinformatic analysis

KAT3B-mediated succinylation of DERL3 suppresses osteogenic differentiation by promoting M1/M2 macrophage polarization

Periodontitis is a chronic inflammatory disease influenced by macrophage polarization. Additionally, succinylation-enriched Porphyromonas gingivalis is a pathogenic factor of periodontitis. However, the role of succinylation in the pathogenesis of periodontitis remains unclear. This study aimed to investigate the effects of a succinyltransferase KAT3B on macrophage polarization, osteogenic differentiation, and the molecular mechanism. Macrophages RAW264.7 were cocultured with MC3T3-E1-differentiated osteoblasts, and macrophage polarization and osteogenic differentiation were evaluated. iTRAQ-based proteomic analysis identified that DERL3 was highly expressed in lipopolysaccharide (LPS)-treated MC3T3-E1 cells. The TLR4/MyD88 pathway is closely related to inflammatory response. Thus, the succinylation of DERL3 and the TLR4/MyD88 pathway were assessed using immunoblotting. The results showed that KAT3B-mediated succinylation was increased in LPS-treated MC3T3-E1 cells and patients with periodontitis. Knockdown of KAT3B inhibited macrophage M1-like polarization and promoted M2-like polarization, thereby promoting osteogenic differentiation in LPS-treated osteoblasts. Mechanically, overexpression of KAT3B promoted the succinylation of DERL3 and stabilized this protein, thereby upregulating DERL3 expression. Rescue experiments showed that DERL3 reversed the promotion of osteogenic differentiation and M2/M1 macrophage polarization caused by KAT3B knockdown. Moreover, DERL3 activated the TLR4/MyD88 pathway, and inhibition of this pathway reversed macrophage polarization and osteogenesis mediated by DERL3. In vivo experiments showed that KAT3B knockdown attenuated experimental periodontitis in rats. In conclusion, silencing of KAT3B promotes osteogenic differentiation by inducing M2/M1 macrophage polarization through the succinylation DERL3, which regulates the TLR4/MyD88 pathway, thereby attenuating periodontitis. These findings suggest that KAT3B may be a promising therapeutic target for periodontitis.

Integrating transcriptomic and polygenic risk scores to enhance predictive accuracy for ischemic stroke subtypes

Ischemic stroke (IS), characterized by complex etiological diversity, is a significant global health challenge. Recent advancements in genome-wide association studies (GWAS) and transcriptomic profiling offer promising avenues for enhanced risk prediction and understanding of disease mechanisms. GWAS summary statistics from the GIGASTROKE Consortium and genetic and phenotypic data from the UK Biobank (UKB) were used. Transcriptome-Wide Association Studies (TWAS) were conducted using FUSION to identify genes associated with IS and its subtypes across eight tissues. Colocalization analysis identified shared genetic variants influencing both gene expression and disease risk. Sum Transcriptome-Polygenic Risk Scores (STPRS) models were constructed by combining polygenic risk scores (PRS) and polygenic transcriptome risk scores (PTRS) using logistic regression. The predictive performance of STPRS was evaluated using the area under the curve (AUC). A Phenome-wide association study (PheWAS) explored associations between STPRS and various phenotypes. TWAS identified 34 susceptibility genes associated with IS and its subtypes. Colocalization analysis revealed 18 genes with a posterior probability (PP) H4 > 75% for joint expression quantitative trait loci (eQTL) and GWAS associations, highlighting their genetic relevance. The STPRS models demonstrated superior predictive accuracy compared to conventional PRS, showing significant associations with numerous UKB phenotypes, including atrial fibrillation and blood pressure. Integrating transcriptomic data with polygenic risk scores through STPRS enhances predictive accuracy for IS and its subtypes. This approach refines our understanding of the genetic and molecular landscape of stroke and paves the way for tailored preventive and therapeutic strategies.

Single-cell sequencing, spatial transcriptome ad periodontitis: Rethink pathogenesis and classification

OBJECTIVE

This narrative review illuminates on the application of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) in periodontitis and highlights the probability of relating cell population and gene signatures to the pathogenesis of the disease for a better diagnosis.

METHODS

An electronic search of the literature in the PubMed database for the keywords, "single cell sequencing" OR "spatial transcriptomics" and "periodontitis" OR "gingiva" OR "oral mucosa" yielded 486 research articles and reviews. After filtering duplicates and careful curation, 22 papers conducted in humans were retained.

RESULTS

The molecular mechanisms underlying periodontitis are complex and involve the interaction of multiple cells and various gene expressions. Most residing cells in periodontal tissues participate in maintaining homeostasis and health, while in addition to infiltrating immune cells contribute to the fight against the bacterial insult.

CONCLUSION

scRNA-seq and ST have provided new insights into the cellular and molecular changes associated with periodontitis for a better diagnosis and clinical outcome. New functions of cells and genes are revealed with these techniques; however, no cells or gene signatures are attributed to periodontitis so far.

An update on periodontal inflammation and bone loss

Periodontal disease is a chronic inflammatory condition that affects the supporting structures of the teeth, including the periodontal ligament and alveolar bone. Periodontal disease is due to an immune response that stimulates gingivitis and periodontitis, and its systemic consequences. This immune response is triggered by bacteria and may be modulated by environmental conditions such as smoking or systemic disease. Recent advances in single cell RNA-seq (scRNA-seq) and in vivo animal studies have provided new insight into the immune response triggered by bacteria that causes periodontitis and gingivitis. Dysbiosis, which constitutes a change in the bacterial composition of the microbiome, is a key factor in the initiation and progression of periodontitis. The host immune response to dysbiosis involves the activation of various cell types, including keratinocytes, stromal cells, neutrophils, monocytes/macrophages, dendritic cells and several lymphocyte subsets, which release pro-inflammatory cytokines and chemokines. Periodontal disease has been implicated in contributing to the pathogenesis of several systemic conditions, including diabetes, rheumatoid arthritis, cardiovascular disease and Alzheimer's disease. Understanding the complex interplay between the oral microbiome and the host immune response is critical for the development of new therapeutic strategies for the prevention and treatment of periodontitis and its systemic consequences.

Identification of Crosstalk Genes between Lung Adenocarcinoma and Periodontitis

BACKGROUND

Lung adenocarcinoma (LUAD) represents a significant global health issue. Smoking contributes to the development of periodontitis and LUAD. The connections between the two are still ambiguous.

METHODS

Based on RNA expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, differentially expressed genes (DEGs) in Periodontitis and LUAD were collected. Protein-protein interaction (PPI) networks were produced by mining genes intersecting with crossover DEGs. Genes in the subnetwork and the top 15 genes of the topology score were defined as the crosstalk gene. Feature selection and diagnostic model construction were conducted based on Recursive Feature Elimination (RFE) and support vector machines (SVM). Additionally, we analyzed the immune cells and signaling pathways influenced by the crosstalk gene.

RESULTS

A total of 29 crossover DEGs between Periodontitis and LUAD were filtered, with 20 genes interacting with them in the PPI network. Five subnetworks with similar interaction patterns in the PPI network were detected. Based on the network topology analysis, genes ranking in the top 15 were used to take the intersection with those genes in the 5 subnetworks. Twelve intersecting genes were identified. Based on RFE and SVM algorithms, FKBP11 and MMP13 were considered as the Crosstalk genes for both Periodontitis and LUAD. The diagnostic model composed of FKBP11 and MMP13 showed excellent diagnostic potential. In addition, we found that FKBP11 and MMP13 influenced Macrophages, M1, T cells, CD8 activity, immune-related pathways, and cell cycle pathways.

CONCLUSION

We identified the crosstalk genes (FKBP11 and MMP13) between periodontitis and LUAD. The two genes affected the comorbidity status between the two diseases through immune cell activity.

Systematic Multiomic Analysis of PKHD1L1 Gene Expression and Its Role as a Predicting Biomarker for Immune Cell Infiltration in Skin Cutaneous Melanoma and Lung Adenocarcinoma

The identification of genetic factors that regulate the cancer immune microenvironment is important for understanding the mechanism of tumor progression and establishing an effective treatment strategy. Polycystic kidney and hepatic disease 1-like protein 1 (PKHD1L1) is a large transmembrane protein that is highly expressed in immune cells; however, its association with tumor progression remains unclear. Here, we systematically analyzed the clinical relevance of PKHD1L1 in the tumor microenvironment in multiple cancer types using various bioinformatic tools. We found that the PKHD1L1 mRNA expression levels were significantly lower in skin cutaneous melanoma (SKCM) and lung adenocarcinoma (LUAD) than in normal tissues. The decreased expression of PKHD1L1 was significantly associated with unfavorable overall survival (OS) in SKCM and LUAD. Additionally, PKHD1L1 expression was positively correlated with the levels of infiltrating B cells, cluster of differentiation (CD)-8+ T cells, and natural killer (NK) cells, suggesting that the infiltration of immune cells could be associated with a good prognosis due to increased PKHD1L1 expression. Gene ontology (GO) analysis also revealed the relationship between PKHD1L1-co-altered genes and the activation of lymphocytes, including B and T cells. Collectively, this study shows that PKHD1L1 expression is positively correlated with a good prognosis via the induction of immune infiltration, suggesting that PKHD1L1 has potential prognostic value in SKCM and LUAD.

SEL1L3 as a link molecular between renal cell carcinoma and atherosclerosis based on bioinformatics analysis and experimental verification

BACKGROUND

Renal cancer, the most common type of kidney cancer, develops in the renal tubular epithelium. Atherosclerosis of the aorta is the primary cause of atherosclerosis. However, the underlying mechanisms remain unclear.

METHODS

The renal clear cell carcinoma RNA sequence profile was obtained from The Cancer Genome Atlas (TCGA) database, and the atherosclerosis datasets GSE28829 and GSE43292 based on GPL570 and GPL6244 was obtained from the Gene Expression Omnibus (GEO) database. The difference and hub genes were identified by the Limma protein-protein interaction (PPI) network in R software. Functional enrichment, survival, and immunoinfiltration analyses were performed. The role of SEL1L3 in the ErbB/PI3K/mTOR signaling pathway, apoptosis, invasion, cell cycle, and inflammation was analyzed using western blotting.

RESULTS

764 DEGs were identified from TCGA Kidney Renal Clear Cell Carcinoma (KIRC) dataset. A total of 344 and 117 DEGs were screened from the GSE14762 and GSE53757 datasets, respectively. Functional enrichment analysis results primarily indicated enrichment in the transporter complex, DNA-binding transcription activator activity, morphogenesis of the embryonic epithelium, stem cell proliferation, adrenal overactivity and so on. Fifteen common DEGs overlapped among the three datasets. The PPI network revealed that SEL1L3 was the core gene. Survival analysis showed that lower SEL1L3 expression levels led to a worse prognosis. Immune cell infiltration analysis showed that SEL1L3 expression was significantly correlated with antibody-drug conjugates (aDC), B cells, eosinophils, interstitial dendritic cells (iDC), macrophages, and more.

CONCLUSIONS

SEL1L3 plays an important role in renal clear cell carcinoma and atherosclerosis and may be a potential link between them.

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.

Identification of Endoplasmic Reticulum Stress-Related Biomarkers of Periodontitis Based on Machine Learning: A Bioinformatics Analysis

Objective

To screen for potential endoplasmic reticulum stress- (ERS-) related biomarkers of periodontitis using machine learning methods and explore their relationship with immune cells.

Methods

Three datasets of periodontitis (GSE10334, GES16134, and GES23586) were obtained from the Gene Expression Omnibus (GEO), and the samples were randomly assigned to the training set or the validation set. ERS-related differentially expressed genes (DEGs) between periodontitis and healthy periodontal tissues were screened and analyzed for GO, KEGG, and DO enrichment. Key DEGs were screened by two machine learning algorithms, LASSO regression and support vector machine-recursive feature elimination (SVM-RFE); then, the potential biomarkers were identified through validation. The infiltration of immune cells of periodontitis was calculated using the CIBERSORT algorithm, and the correlation between immune cells and potential biomarkers was specifically analyzed through the Spearman method.

Results

We obtained 36 ERS-related DEGs of periodontitis from the training set, from which 11 key DEGs were screened by further machine learning. SERPINA1, ERLEC1, and VWF showed high diagnostic values (AUC > 0.85), so they were considered as potential biomarkers for periodontitis. According to the results of the immune cell infiltration analysis, these three potential biomarkers showed marked correlations with plasma cells, neutrophils, resting dendritic cells, resting mast cells, and follicular helper T cells.

Conclusions

Three ERS-related genes, SERPINA1, ERLEC1, and VWF, showed valuable biomarker potential for periodontitis, which provide a target base for future studies on early diagnosis and treatment of periodontitis.

Identifying key genes in CD4+ T cells of systemic lupus erythematosus by integrated bioinformatics analysis

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by excessive activation of T and B lymphocytes and breakdown of immune tolerance to autoantigens. Despite several mechanisms including the genetic alterations and inflammatory responses have been reported, the overall signature genes in CD4⁺ T cells and how they affect the pathological process of SLE remain to be elucidated. This study aimed to identify the crucial genes, potential biological processes and pathways underlying SLE pathogenesis by integrated bioinformatics. The gene expression profiles of isolated peripheral CD4⁺ T cells from SLE patients with different disease activity and healthy controls (GSE97263) were analyzed, and 14 co-expression modules were identified using weighted gene co-expression network analysis (WGCNA). Some of these modules showed significantly positive or negative correlations with SLE disease activity, and primarily enriched in the regulation of type I interferon and immune responses. Next, combining time course sequencing (TCseq) with differentially expressed gene (DEG) analysis, crucial genes in lupus CD4⁺ T cells were revealed, including some interferon signature genes (ISGs). Among these genes, we identified 4 upregulated genes (PLSCR1, IFI35, BATF2 and CLDN5) and 2 downregulated genes (GDF7 and DERL3) as newfound key genes. The elevated genes showed close relationship with the SLE disease activity. In general, our study identified 6 novel biomarkers in CD4⁺ T cells that might contribute to the diagnosis and treatment of SLE.