Identification and integrated analysis of differentially expressed long non-coding RNAs associated with periodontitis in humans

Expanding and Enriching the LncRNA Gene-Disease Landscape Using the GeneCaRNA Database

The GeneCaRNA human gene database is a member of the GeneCards Suite. It presents ~280,000 human non-coding RNA genes, identified algorithmically from ~690,000 RNAcentral transcripts. This expands by ~tenfold the ncRNA gene count relative to other sources. GeneCaRNA thus contains ~120,000 long non-coding RNAs (LncRNAs, >200 bases long), including ~100,000 novel genes. The latter have sparse functional information, a vast terra incognita for future research. LncRNA genes are uniformly represented on all nuclear chromosomes, with 10 genes on mitochondrial DNA. Data obtained from MalaCards, another GeneCards Suite member, finds 1547 genes associated with 1 to 50 diseases. About 15% of the associations portray experimental evidence, with cancers tending to be multigenic. Preliminary text mining within GeneCaRNA discovers interactions of lncRNA transcripts with target gene products, with 25% being ncRNAs and 75% proteins. GeneCaRNA has a biological pathways section, which at present shows 131 pathways for 38 lncRNA genes, a basis for future expansion. Finally, our GeneHancer database provides regulatory elements for ~110,000 lncRNA genes, offering pointers for co-regulated genes and genetic linkages from enhancers to diseases. We anticipate that the broad vista provided by GeneCaRNA will serve as an essential guide for further lncRNA research in disease decipherment.

Identifying genes for regulating osteogenic differentiation of human periodontal ligament stem cells in inflammatory environments by bioinformatics analysis

BACKGROUND AND OBJECTIVES

Periodontitis is an immuno-inflammatory disease caused by dental plaque biofilms and inflammations. The regeneration of bone tissue in inflammatory environment is of great significance for the treatment of periodontal disease, but the specific molecular mechanism of bone formation in periodontitis still needs further exploration. The objective of this study was to identify key osteogenesis-related genes (ORGs) in periodontitis.

METHODS

We used two datasets from the Gene Expression Omnibus (GEO) database to find differentially expressed mRNAs and miRNAs, further performed Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then we predicted the downstream genes of the differentially expressed miRNAs (DEMs) by the TargetScan database and established a miRNA-mRNA regulatory network. Finally, the osteogenic mechanism of periodontitis was explored through quantitative real-time PCR (qRT-PCR) by inducing inflammatory environment and osteogenic differentiation of hPDLSCs.

RESULTS

Through differential expression analysis and prediction of downstream target genes of DEMs, we created a miRNA-mRNA regulatory network consisting of 29 DEMs and 11 differentially expressed osteogenesis-related genes (DEORGs). In addition, the qRT-PCR results demonstrated that BTBD3, PLAT, AKAP12, SGK1, and GLCE expression levels were significantly upregulated, while those of TIMP3, ZCCHC14, LIN7A, DNAH6, NNT, and ITGA6 were downregulated under the dual effects of inflammatory stimulation and osteogenic induction.

CONCLUSION

DEORGs might be important factors in the osteogenic phase of periodontitis, and the miRNA-mRNA network may shed light on the clarification of the role and mechanism of osteogenesis in periodontitis and contribute to the development of novel therapeutic strategies.

Decoding the role of long non-coding RNAs in periodontitis: A comprehensive review

Periodontitis is an inflammatory disease characterized by the pathological loss of alveolar bone and the adjacent periodontal ligament. It is considered a disease that imposes a substantial health burden, with an incidence rate of 20-50%. The etiology of periodontitis is multifactorial, with genetic factors accounting for approximately half of severe cases. Studies have revealed that long non-coding RNAs (lncRNAs) play a pivotal role in periodontitis pathogenesis. Accumulating evidence suggests that lncRNAs have distinct regulatory mechanisms, enabling them to control numerous vital processes in periodontal cells, including osteogenic differentiation, inflammation, proliferation, apoptosis, and autophagy. In this review, we summarize the diverse roles of lncRNAs in the pathogenesis of periodontitis, shedding light on the underlying mechanisms of disease development. By highlighting the potential of lncRNAs as biomarkers and therapeutic targets, this review offers a new perspective on the diagnosis and treatment of periodontitis, paving the way for further investigation into the field of lncRNA-based therapeutics.

Development of Ferroptosis-Associated ceRNA Network in Periodontitis

OBJECTIVES

Periodontitis is a chronic inflammatory illness that may lead to tooth loosening and even loss, and its pathogenesis is not fully understood. Ferroptosis is an iron-dependent, regulated cell death. The present study aims to find the key ferroptosis-related genes (FRGs) in periodontitis and develop an mRNA-miRNA-lncRNA network to deeply explore the pathogenesis of periodontitis.

METHODS

Data from the Gene Expression Omnibus (GEO) database and FerrDb database were downloaded to discover the differentially expressed mRNA, miRNA, and FRGs. Functional enrichment analysis was conducted for the differentially expressed FRGs (DE-FRGs), including gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network analysis. Targetscan and miRtarbase were used to estimate the miRNAs that DE-FRGs may interact with, whilst StarBase v3.0 was used for lncRNA-miRNA interaction.

RESULTS

Seven DE-FRGs were identified through differential expression analysis. Interleukin 1 beta (IL1B) interacted with XBP1 and MMP13 in the PPI network. After taking the intersection between DE-miRNAs and predicted miRNAs, a ceRNA network containing IL1B, has-miR-185, has-miR-204, has-miR-211, has-miR-4306, and 28 lncRNAs was established.

CONCLUSIONS

Seven FRGs in periodontitis were identified, which might promote deeper understanding of ferroptosis in periodontitis.

Whole-transcriptome analysis of periodontal tissue and construction of immune-related competitive endogenous RNA network

Background

In periodontitis, noncoding RNAs may play a regulatory role in the immune microenvironment through competitive endogenous RNA. We aimed to profile noncoding RNA expression and construct immune-related ceRNA network in periodontitis.

Methods

Five inflamed periodontal tissue and five healthy gingivae were collected for whole-transcriptome sequencing. Differential gene, functional enrichment, and protein–protein interaction network analysis were performed to explore the function of differentially expressed genes. CIBERSORTx was used to analyze level of immune cell infiltration in the periodontal tissue. An immune-related competitive endogenous RNA network was constructed and expression of key regulators in the network was validated.

Results

Compared with healthy gingiva, 200 mRNAs, 90 long noncoding RNAs, 65 microRNAs, and 518 circular RNAs were differentially expressed, and cell chemotaxis was significantly enhanced in inflamed periodontal tissue. Immune cell infiltration analysis showed that neutrophils, macrophages M1, T follicular helper cells, and naive B cells were significantly increased in periodontitis. Key regulators including JUN, FOS, THBS1, KLF2, WIF1, were identified and their expression was then validated.

Conclusion

We constructed an immune-related competitive endogenous RNA network in periodontal tissue, which provided new insights into immune homeostasis in periodontitis and laid a foundation for further study of noncoding RNAs. Key regulators in this network may be promising targets for future periodontitis treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02401-0.

Identification of microRNA–mRNA–TF regulatory networks in periodontitis by bioinformatics analysis

Background

Periodontitis is a complex infectious disease with various causes and contributing factors. The aim of this study was to identify key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct a miRNA–mRNA–TF regulatory networks to investigate the underlying molecular mechanism in periodontitis.

Methods

The GSE54710 miRNA microarray dataset and the gene expression microarray dataset GSE16134 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were screened using the “limma” package in R. The intersection of the target genes of candidate DEMis and DEMs were considered significant DEMs in the regulatory network. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Subsequently, DEMs were uploaded to the STRING database, a protein–protein interaction (PPI) network was established, and the cytoHubba and MCODE plugins were used to screen out key hub mRNAs and significant modules. Ultimately, to investigate the regulatory network underlying periodontitis, a global triple network including miRNAs, mRNAs, and TFs was constructed using Cytoscape software.

Results

8 DEMis and 121 DEMs were found between the periodontal and control groups. GO analysis showed that mRNAs were most significantly enriched in positive regulation of the cell cycle, and KEGG pathway analysis showed that mRNAs in the regulatory network were mainly involved in the IL-17 signalling pathway. A PPI network was constructed including 81 nodes and 414 edges. Furthermore, 12 hub genes ranked by the top 10% genes with high degree connectivity and five TFs, including SRF, CNOT4, SIX6, SRRM3, NELFA, and ONECUT3, were identified and might play crucial roles in the molecular pathogenesis of periodontitis. Additionally, a miRNA–mRNA–TF coregulatory network was established.

Conclusion

In this study, we performed an integrated analysis based on public databases to identify specific TFs, miRNAs, and mRNAs that may play a pivotal role in periodontitis. On this basis, a TF–miRNA–mRNA network was established to provide a comprehensive perspective of the regulatory mechanism networks of periodontitis.

The Expression and Regulatory Roles of Long Non-Coding RNAs in Periodontal Ligament Cells: A Systematic Review

Periodontal ligament (PDL) cells play a pivotal role in periodontal and bone homeostasis and have promising potential for regenerative medicine and tissue engineering. There is compelling evidence that long non-coding RNAs (lncRNAs) are differentially expressed in PDL cells compared to other cell types and that these lncRNAs are involved in a variety of biological processes. This study systematically reviews the current evidence regarding the expression and regulatory functions of lncRNAs in PDL cells during various biological processes. A systematic search was conducted on PubMed, the Web of Science, Embase, and Google Scholar to include articles published up to 1 July 2021. Original research articles that investigated the expression or regulation of lncRNAs in PDL cells were selected and evaluated for a systematic review. Fifty studies were ultimately included, based on our eligibility criteria. Thirteen of these studies broadly explored the expression profiles of lncRNAs in PDL cells using microarray or RNA sequencing. Nineteen studies investigated the mechanisms by which lncRNAs regulate osteogenic differentiation in PDL cells. The remaining 18 studies investigated the mechanism by which lncRNAs regulate the responses of PDL cells to various stimuli, namely, lipopolysaccharide-induced inflammation, tumor necrosis factor alpha-induced inflammation, mechanical stress, oxidative stress, or hypoxia. We systematically reviewed studies on the expression and regulatory roles of lncRNAs in diverse biological processes in PDL cells, including osteogenic differentiation and cellular responses to inflammation, mechanical stress, and other stimuli. These results provide new insights that may guide the development of lncRNA-based therapeutics for periodontal and bone regeneration.

Comprehensive analysis of the long noncoding RNA-associated competitive endogenous RNA network in the osteogenic differentiation of periodontal ligament stem cells

BACKGROUD

The mechanism implicated in the osteogenesis of human periodontal ligament stem cells (PDLSCs) has been investigated for years. Previous genomics data analyses showed that long noncoding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) have significant expression differences between induced and control human PDLSCs. Competing for endogenous RNAs (ceRNA), as a widely studied mechanism in regenerative medicine, while rarely reported in periodontal regeneration. The key lncRNAs and their ceRNA network might provide new insights into molecular therapies of periodontal regeneration based on PDLSCs.

RESULTS

Two networks reflecting the relationships among differentially expressed RNAs were constructed. One ceRNA network was composed of 6 upregulated lncRNAs, 280 upregulated mRNAs, and 18 downregulated miRNAs. The other network contained 33 downregulated lncRNAs, 73 downregulated mRNAs, and 5 upregulated miRNAs. Functional analysis revealed that 38 GO terms and 8 pathways related with osteogenesis were enriched. Twenty-four osteogenesis-related gene-centred lncRNA-associated ceRNA networks were successfully constructed. Among these pathways, we highlighted MAPK and TGF-beta pathways that are closely related to osteogenesis. Subsequently, subnetworks potentially linking the GO:0001649 (osteoblast differentiation), MAPK and TGF-beta pathways were constructed. The qRT-PCR validation results were consistent with the microarray analysis.

CONCLUSION

We construct a comprehensively identified lncRNA-associated ceRNA network might be involved in the osteogenesis of PDLSCs, which could provide insights into the regulatory mechanisms and treatment targets of periodontal regeneration.

mRNA and long non-coding RNA expression profiling of human periodontal ligament cells under tension loading

Objective

This study explored the expression profiles of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) in human periodontal ligament (PDL) cells subjected to tensile loading.

Methods

PDL cells were isolated from the teeth of five healthy individuals, cultured and then exposed to tensile loading. RNA sequencing was performed to explore the mRNA and lncRNA expression profiles with or without tensile loading. Differential expression, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to reveal enriched biological functions and signal transduction pathways. Quantitative polymerase chain reaction (qPCR) was performed to validate the expression of specific mRNAs and lncRNAs associated with the enriched pathways.

Results

Tensile loading significantly enhanced the osteogenic potential of PDL cells. Overall, 1438 mRNAs (860 up- and 578 down-regulated) and 195 lncRNAs (107 up- and 88 down-regulated) were differentially expressed (adjusted P-value <0.05) in the tensile loading group versus the control group. GO and KEGG analyses of the differentially expressed genes indicated significant enrichment in osteogenesis-related biological processes and intracellular signal transduction pathways (e.g. the PI3K–Akt pathway), respectively. The qPCR analysis validated the expression levels of five selected mRNAs (EGFR, FGF5, VEGFA, HIF1A, and FOXO1) and three selected lncRNAs (CYTOR, MIR22HG, and SNHG3).

Limitation

Further studies are warranted to validate the mechanisms regulating tension-induced bone remodelling in PDL cells and potential regulation by the identified lncRNAs.

Conclusion

The notably altered mRNA and lncRNA expression profiles in PDL cells under tensile loading enhance our mechanistic understanding of tension-induced osteogenesis.