Cell Type-Specific Decomposition of Gingival Tissue Transcriptomes

Yunnan Baiyao Might Mitigate Periodontitis Bone Destruction by Inhibiting Autophagy and Promoting Osteoblast Differentiation in vivo, ex vivo and in vitro

Background and Objective

Periodontitis is an inflammatory disease that eventually destroys tooth-supporting tissue. Yunnan Baiyao (YNBY), a traditional Chinese medicine compound with haemostatic and anti-inflammatory properties has shown therapeutic potential in several diseases. Our previous study revealed that YNBY suppressed osteoclast differentiation in periodontitis. The purpose of this study is to investigate the influences of YNBY on osteoblasts and explore its potential mechanisms.

Materials and Methods

A rat periodontitis model was established by ligation of maxillary second molars. After the end of modelling, histopathological observation by hematoxylin-eosin (HE) staining and Masson trichrome staining, detection of bone resorption by Micro-CT scanning, detection of osteoclasts by tartrate-resistant acid phosphatase (TRAP) staining, expression of osteocalcin (OCN) and microtubule-associated protein 1 light chain 3 (LC3) by immunohistochemistry. Lipopolysaccharides was used to irritate MC3T3-E1 osteoblastic cells and ex vivo calvarial organ as an in vitro model of inflammation. CCK-8 assay was performed to examine the toxicity of YNBY to MC3T3-E1 osteoblastic cells. Osteogenesis was assessed with alizarin red staining, immunofluorescence staining, Western blot and immunohistochemical staining. Transmission electron microscopy, fluorescent double staining, Western blot and immunohistochemical staining were employed to detect autophagy.

Results

Histological and micro-CT analyses revealed that YNBY gavage reduced bone loss caused by experimental periodontitis and upregulated osteogenic proteins in vivo. YNBY attenuated the production of autophagy-related proteins in periodontitis rats. Additionally, YNBY promoted osteogenesis by inhibiting inflammation-induced autophagy in vitro. Furthermore, YNBY suppressed LPS-mediated bone resorption and promoted the production of osteoblast-related proteins in inflamed calvarial tissues ex vivo.

Conclusion

This study demonstrated, through in vivo, in vitro and ex vivo experiments, that YNBY promoted osteoblast differentiation by suppressing autophagy, which markedly alleviated bone destruction caused by periodontitis.

Macrophage-related gingival transcriptomic patterns and microbiome alterations in experimental periodontitis in nonhuman primates

OBJECTIVE

This study examined the microbiome features specifically related to host macrophage polarization in health, initiation and progression of periodontitis, and in resolution samples using a nonhuman primate model of ligature-induced periodontitis.

BACKGROUND

The oral microbiome is a complex of bacterial phyla, genera, and species acquired early in life into the individual autochthonous oral ecology. The microbiome changes overtime in response to both intrinsic and extrinsic stressors, and transitions to a dysbiotic ecology at sites of periodontal lesions.

METHODS

Comparisons were made between the microbial and host features in young (≤7 years) and adult (≥12 years) cohorts of animals. Footprints of macrophage-related genes in the gingival tissues were evaluated using expression profiles including M0, M1, and M2 related genes.

RESULTS

Within the gingival tissues, similar macrophage-related gene patterns were observed with significant increases with disease initiation and continued elevation throughout disease in both age groups. Approximately, 70% of the taxa were similar in relative abundance between the two groups; however, the adults showed a large number of OTUs that were significantly altered compared with the younger animals. Developing a correlation map identified three major node levels of interactions that comprised approximately ⅓ of the Operational Taxonomic Units (OTUs) that dominated the microbiomes across the samples. Also noted was a much greater frequency of significant correlations of individual OTUs with the macrophage phenotype markers, compared with disease and resolution samples in both age groups, with a greater frequency in the younger group. Moreover, these correlations were assigned to differentially expressed genes representing M0, M1, and M2-related phenotypes. A cluster analyses across the macrophage-related transcriptome and the OTUs demonstrated multiple somewhat distinct bacterial consortia, incorporating both commensal and putative pathogens, linked to the gene responses that differed in health, disease, and resolution samples. Finally, there were minimal alterations in the OTUs in individual clusters with specific macrophage-related responses in the younger group, while in the adult samples substantial variations were noted with genes from all macrophage phenotypes.

CONCLUSIONS

The results confirmed important features that could reflect macrophage polarization in periodontal lesions, and provided some initial data supporting specific members of the oral microbiome feature prominently related to specific gene response patterns consistent with macrophages in the gingival tissues.

Development of a classification model and an immune-related network based on ferroptosis in periodontitis

BACKGROUND AND OBJECTIVES

Periodontitis is an immunoinflammatory disease characterized by irreversible periodontal attachment loss and bone destruction. Ferroptosis is a kind of immunogenic cell death that depends on the participation of iron ions and is involved in various inflammatory and immune processes. However, information regarding the relationship between ferroptosis and immunomodulation processes in periodontitis is extremely limited. The purpose of this study was to investigate the correlation between ferroptosis and immune responses in periodontitis.

METHODS

Gene expression profiles of gingivae were collected from the Gene Expression Omnibus data portal. After detecting differentially expressed ferroptosis-related genes (FRGs), we used univariate logistic regression analysis followed by logistic least absolute shrinkage and selection operator (LASSO) regression to establish a ferroptosis-related classification model in an attempt to accurately distinguish periodontitis gingival tissues from healthy samples. The infiltration level of immunocytes in periodontitis was then assessed through single-sample gene-set enrichment analysis. Subsequently, we screened out immune-related genes by weighted correlation network analysis and protein-protein interaction (PPI) analysis and constructed an immune-related network based on FRGs and immune-related genes.

RESULTS

A total of 24 differentially expressed FRGs were detected, and an 8-FRG combined signature constituted the classification model. The established model showed outstanding discriminating ability according to the results of receiver operating characteristic (ROC) curve analysis. In addition, the periodontitis samples had a higher degree of immunocyte infiltration. Activated B cells had the strongest positive correlation while macrophages had a strong negative correlation with certain FRGs, and we found that XBP1, ALOX5 and their interacting genes might be crucial genes in the immune-related network.

CONCLUSIONS

The FRG-based classification model had a satisfactory determination ability, which could bring new insights into the pathogenesis of periodontitis. Those genes in the immune-related network, especially hub genes along with XBP1 and ALOX5, would have the potential to serve as promising targets of immunomodulatory treatments for periodontitis.

Gingival transcriptomic patterns of macrophage polarization during initiation, progression, and resolution of periodontitis

Phenotypic and functional heterogeneity of macrophages is clearly a critical component of their effective functions in innate and adaptive immunity. This investigation hypothesized that altered profiles of gene expression in gingival tissues in health, disease, and resolution would reflect changes in macrophage phenotypes occurring in these tissues. The study used a nonhuman primate model to evaluate gene expression profiles as footprints of macrophage variation using a longitudinal experimental model of ligature-induced periodontitis in animals from 3 to 23 years of age to identify aging effects on the gingival environment. Significant differences were observed in distribution of expressed gene levels for M0, M1, and M2 macrophages in healthy tissues with the younger animals showing the least expression. M0 gene expression increased with disease in all but the aged group, while M1 was increased in adult and young animals, and M2 in all age groups, as early as disease initiation (within 0.5 months). Numerous histocompatibility genes were increased with disease, except in the aged samples. An array of cytokines/chemokines representing both M1 and M2 cells were increased with disease showing substantial increases with disease initiation (e.g. IL1A, CXCL8, CCL19, CCL2, CCL18), although the aged tissues showed a more limited magnitude of change across these macrophage genes. The analytics of macrophage genes at sites of gingival health, disease, and resolution demonstrated distinct profiles of host response interactions that may help model the disease mechanisms occurring with the formation of a periodontal lesion.

Differential Expression, Functional and Machine Learning Analysis of High-Throughput -Omics Data Using Open-Source Tools

Today, -omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseases, offering a large advantage over earlier "candidate" gene or pathway analyses. A primary goal in the analysis of these high-throughput assays is the detection of those features among several thousand that differ between different groups of samples. In the context of oral biology, our group has successfully utilized -omics technology to identify key molecules and pathways in different diagnostic entities of periodontal disease.A major issue when inferring biological information from high-throughput -omics studies is the fact that the sheer volume of high-dimensional data generated by contemporary technology is not appropriately analyzed using common statistical methods employed in the biomedical sciences. Furthermore, machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Herein, we outline a robust and well-accepted bioinformatics workflow for the initial analysis of -omics data using open-source tools. We outline a differential expression analysis pipeline that can be used for data from both arrays and sequencing experiments, and offers the possibility to account for random or fixed effects. Furthermore, we present an overview of the possibilities for a functional analysis of the obtained data including subsequent machine learning approaches in form of (i) supervised classification algorithms in class validation and (ii) unsupervised clustering in class discovery.

Genome-Wide Analysis of Periodontal and Peri-implant Cells and Tissues

-Omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ, or tissue sample, are powerful means of generating comprehensive genome-level data sets on complex diseases. We have systematically assessed the transcriptome, microbiome, miRNome, and methylome of gingival and peri-implant tissues from human subjects and further studied the transcriptome of primary cells ex vivo, or in vitro after infection with periodontal pathogens.Our data offer new insight on the pathophysiology underlying periodontal and peri-implant diseases, a possible route to a better and earlier diagnosis of these highly prevalent chronic inflammatory diseases and thus, to a personalized and efficient treatment approach.Herein, we outline the laboratory steps required for the processing of periodontal cells and tissues for -omics analyses using current microarrays or next-generation sequencing technology.

Expression of periodontitis susceptibility genes in human gingiva using single-cell RNA sequencing

OBJECTIVE

Single-cell transcriptomics was used to determine the possible cell-type specificity of periodontitis susceptibility genes.

BACKGROUND

The last decade has witnessed remarkable advances in the field of human genomics. Despite many advances, the genetic factors associated with or contributing to the periodontitis pathogenesis have only been identified to a limited extent and are often poorly validated. Confirming whether a given single nucleotide polymorphism has an association with periodontitis requires a robust mechanistic explanation on the functional consequences of a given genetic variant.

METHODS

We globally assessed the expression of 26 disease-associated genes identified by GWAS within the gingival mucosa. A total of 12 411 cells from 4 different donors were analysed. Differentially expressed genes were analysed using Seurat, a non-parametric Wilcoxon rank sum test. The minimum threshold for significance was defined as p < .05.

RESULTS

This exploration at a cellular-level suggests diverse populations contributing to disease pathogenesis, with macrophages expressing a higher number of the analysed disease-associated genes. IL1B, PTGS2, FCGR2A, IL10 and IL1A specifically showed a more restricted expression in the myeloid lineages.

CONCLUSION

This short report combines human genetics and single-cell genomics to better understand periodontitis by mapping variants to predict their cells of action and putative functions. These findings seem to suggest that innate cell dysfunction is linked to disease susceptibility.

Comparative Analysis of Gene Expression Patterns for Oral Epithelial Cell Functions in Periodontitis

The structure and function of epithelial cells are critical for the construction and maintenance of intact epithelial surfaces throughout the body. Beyond the mechanical barrier functions, epithelial cells have been identified as active participants in providing warning signals to the host immune and inflammatory cells and in communicating various detailed information on the noxious challenge to help drive specificity in the characteristics of the host response related to health or pathologic inflammation. Rhesus monkeys were used in these studies to evaluate the gingival transcriptome for naturally occurring disease samples (GeneChip® Rhesus Macaque Genome Array) or for ligature-induced disease (GeneChip® Rhesus Gene 1.0 ST Array) to explore up to 452 annotated genes related to epithelial cell structure and functions. Animals were distributed by age into four groups: ≤ 3 years (young), 3–7 years (adolescent), 12–16 years (adult), and 18–23 years (aged). For naturally occurring disease, adult and aged periodontitis animals were used, which comprised 34 animals (14 females and 20 males). Groups of nine animals in similar age groups were included in a ligature-induced periodontitis experiment. A buccal gingival sample from either healthy or periodontitis-affected tissues were collected, and microarray analysis performed. The overall results of this investigation suggested a substantial alteration in epithelial cell functions that occurs rapidly with disease initiation. Many of these changes were prolonged throughout disease progression and generally reflect a disruption of normal cellular functions that would presage the resulting tissue destruction and clinical disease measures. Finally, clinical resolution may not signify biological resolution and represent a continued risk for disease that may require considerations for additional biologically specific interventions to best manage further disease.

Gingival Transcriptome of Innate Antimicrobial Factors and the Oral Microbiome With Aging and Periodontitis

The epithelial barrier at mucosal sites comprises an important mechanical protective feature of innate immunity, and is intimately involved in communicating signals of infection/tissue damage to inflammatory and immune cells in these local environments. A wide array of antimicrobial factors (AMF) exist at mucosal sites and in secretions that contribute to this innate immunity. A non-human primate model of ligature-induced periodontitis was used to explore characteristics of the antimicrobial factor transcriptome (n = 114 genes) of gingival biopsies in health, initiation and progression of periodontal lesions, and in samples with clinical resolution. Age effects and relationship of AMF to the dominant members of the oral microbiome were also evaluated. AMF could be stratified into 4 groups with high (n = 22), intermediate (n = 29), low (n = 18) and very low (n = 45) expression in healthy adult tissues. A subset of AMF were altered in healthy young, adolescent and aged samples compared with adults (e.g., APP, CCL28, DEFB113, DEFB126, FLG2, PRH1) and were affected across multiple age groups. With disease, a greater number of the AMF genes were affected in the adult and aged samples with skewing toward decreased expression, for example WDC12, PGLYRP3, FLG2, DEFB128, and DEF4A/B, with multiple age groups. Few of the AMF genes showed a >2-fold increase with disease in any age group. Selected AMF exhibited significant positive correlations across the array of AMF that varied in health and disease. In contrast, a rather limited number of the AMF significantly correlated with members of the microbiome; most prominent in healthy samples. These correlated microbes were different in younger and older samples and differed in health, disease and resolution samples. The findings supported effects of age on the expression of AMF genes in healthy gingival tissues showing a relationship to members of the oral microbiome. Furthermore, a dynamic expression of AMF genes was related to the disease process and showed similarities across the age groups, except for low/very low expressed genes that were unaffected in young samples. Targeted assessment of AMF members from this large array may provide insight into differences in disease risk and biomolecules that provide some discernment of early transition to disease.

Decoding the role of macrophages in periodontitis and type 2 diabetes using single-cell RNA-sequencing

Macrophages are resident myeloid cells in the gingival tissue which control homeostasis and play a pivotal role in orchestrating the immune response in periodontitis. Cell heterogeneity and functional phenotypes of macrophage subpopulations in periodontitis remain elusive. Here, we isolated gingival tissue from periodontitis-affected and healthy sites of patients with and without type 2 diabetes mellitus (T2DM). We then used single-cell RNA-sequencing (scRNA-seq) to define the heterogeneity of tissue-resident macrophages in gingival tissue in health vs. periodontitis. scRNA-seq demonstrated an unforeseen gene expression heterogeneity among macrophages in periodontitis and showed transcriptional and signaling heterogeneity of identified subsets in an independent cohort of patients with periodontitis and T2DM. Our bioinformatic inferences indicated divergent expression profiles in macrophages driven by transcriptional regulators CIITA, RELA, RFX5, and RUNX2. Macrophages in periodontitis expressed both pro-inflammatory and anti-inflammatory markers and their polarization was not mutually exclusive. The majority of macrophages in periodontitis expressed the monocyte lineage marker CD14, indicating their bone marrow lineage. We also found high expression and activation of RELA, a subunit of the NF-κB transcription factor complex, in gingival macrophages of periodontitis patients with T2DM. Our data suggested that heterogeneity and hyperinflammatory activation of macrophages may be relevant to the pathogenesis and outcomes of periodontitis, and may be further augmented in patients with T2DM.

Differential DNA methylation and mRNA transcription in gingival tissues in periodontal health and disease

AIM

We investigated differential DNA methylation in gingival tissues in periodontal health, gingivitis, and periodontitis, and its association with differential mRNA expression.

MATERIALS AND METHODS

Gingival tissues were harvested from individuals and sites with clinically healthy and intact periodontium, gingivitis, and periodontitis. Samples were processed for differential DNA methylation and mRNA expression using the IlluminaEPIC (850 K) and the IlluminaHiSeq2000 platforms, respectively. Across the three phenotypes, we identified differentially methylated CpG sites and regions, differentially expressed genes (DEGs), and genes with concomitant differential methylation at their promoters and expression were identified. The findings were validated using our earlier databases using HG-U133Plus2.0Affymetrix microarrays and Illumina (450 K) methylation arrays.

RESULTS

We observed 43,631 differentially methylated positions (DMPs) between periodontitis and health, and 536 DMPs between gingivitis and health (FDR < 0.05). On the mRNA level, statistically significant DEGs were observed only between periodontitis and health (n = 126). Twelve DEGs between periodontitis and health (DCC, KCNA3, KCNA2, RIMS2, HOXB7, PNOC, IRX1, JSRP1, TBX1, OPCML, CECR1, SCN4B) were also differentially methylated between the two phenotypes. Spearman correlations between methylation and expression in the EPIC/mRNAseq dataset were largely replicated in the 450 K/Affymetrix datasets.

CONCLUSIONS

Concomitant study of DNA methylation and gene expression patterns may identify genes whose expression is epigenetically regulated in periodontitis.