A combined epigenome- and transcriptome-wide association study of the oral masticatory mucosa assigns CYP1B1 a central role for epithelial health in smokers

Multivariate analysis of oral mucosal ulcers during orthodontic treatment

BACKGROUND

Orthodontic treatment can easily cause local soft tissue reactions in the oral cavity of patients under mechanical stress, leading to oral mucosal ulcers and affecting their quality of life. At present, only limited literature has explored the factors leading to oral ulcers in orthodontic treatment, and these research results are still controversial.

AIM

To investigate the current status and related factors of oral mucosal ulcers during orthodontic treatment, aiming to provide a valuable reference for preventing this disease in clinical practice.

METHODS

A total of 587 patients who underwent orthodontic treatment at the Peking University School of Stomatology and Hospital of Stomatology between 2020 and 2022 were selected and allocated to an observation or control group according to the incidence of oral mucosal ulcers during orthodontic therapy. A questionnaire survey was constructed to collect patient data, including basic information, lifestyle and eating habits, treatment details, mental factors, and trace element levels, and a comparative analysis of this data was performed between the two groups.

RESULTS

A logistic regression model with oral ulcers as the dependent variable was established. The regression results showed that age (≥ 60 years: odds ratio [OR]: 6.820; 95% confidence interval [CI]: 2.226–20.893), smoking history (smoking: OR: 4.434; 95%CI: 2.527–7.782), toothbrush hardness (hard: OR: 2.804; 95%CI: 1.746–4.505), dietary temperature (hot diet: OR: 1.399; 95%CI: 1.220–1.722), treatment course (> 1 year: OR: 3.830; 95%CI: 2.203–6.659), and tooth brushing frequency (> 1 time per day: OR: 0.228; 95%CI: 0.138–0.377) were independent factors for oral mucosal ulcers (P < 0.05). Furthermore, Zn level (OR: 0.945; 95%CI: 0.927–0.964) was a protective factor against oral ulcers, while the SAS (OR: 1.284; 95%CI: 1.197–1.378) and SDS (OR: 1.322; 95%CI: 1.231–1.419) scores were risk factors.

CONCLUSION

Age ≥ 60 years, smoking history, hard toothbrush, hot diet, treatment course for > 1 year, tooth brushing frequency of ≤ 1 time per day, and mental anxiety are independent risk factors for oral mucosal ulcers. Therefore, these factors should receive clinical attention and be incorporated into the development and optimization of preventive strategies for reducing oral ulcer incidence.

lncRNA CDKN2B-AS1 regulates collagen expression

The long noncoding RNA CDKN2B-AS1 harbors a major coronary artery disease risk haplotype, which is also associated with progressive forms of the oral inflammatory disease periodontitis as well as myocardial infarction (MI). Despite extensive research, there is currently no broad consensus on the function of CDKN2B-AS1 that would explain a common molecular role of this lncRNA in these diseases. Our aim was to investigate the role of CDKN2B-AS1 in gingival cells to better understand the molecular mechanisms underlying the increased risk of progressive periodontitis. We downregulated CDKN2B-AS1 transcript levels in primary gingival fibroblasts with LNA GapmeRs. Following RNA-sequencing, we performed differential expression, gene set enrichment analyses and Western Blotting. Putative causal alleles were searched by analyzing associated DNA sequence variants for changes of predicted transcription factor binding sites. We functionally characterized putative functional alleles using luciferase-reporter and antibody electrophoretic mobility shift assays in gingival fibroblasts and HeLa cells. Of all gene sets analysed, collagen biosynthesis was most significantly upregulated (Padj=9.7 × 10- 5 (AUC > 0.65) with the CAD and MI risk gene COL4A1 showing strongest upregulation of the enriched gene sets (Fold change = 12.13, Padj = 4.9 × 10- 25). The inflammatory "TNFA signaling via NFKB" gene set was downregulated the most (Padj=1 × 10- 5 (AUC = 0.60). On the single gene level, CAPNS2, involved in extracellular matrix organization, was the top upregulated protein coding gene (Fold change = 48.5, P < 9 × 10- 24). The risk variant rs10757278 altered a binding site of the pathogen responsive transcription factor STAT1 (P = 5.8 × 10- 6). rs10757278-G allele reduced STAT1 binding 14.4% and rs10757278-A decreased luciferase activity in gingival fibroblasts 41.2% (P = 0.0056), corresponding with GTEx data. CDKN2B-AS1 represses collagen gene expression in gingival fibroblasts. Dysregulated collagen biosynthesis through allele-specific CDKN2B-AS1 expression in response to inflammatory factors may affect collagen synthesis, and in consequence tissue barrier and atherosclerotic plaque stability.

The association of cigarette smoking with DNA methylation and gene expression in human tissue samples

Cigarette smoking adversely affects many aspects of human health, and epigenetic responses to smoking may reflect mechanisms that mediate or defend against these effects. Prior studies of smoking and DNA methylation (DNAm), typically measured in leukocytes, have identified numerous smoking-associated regions (e.g., AHRR). To identify smoking-associated DNAm features in typically inaccessible tissues, we generated array-based DNAm data for 916 tissue samples from the GTEx (Genotype-Tissue Expression) project representing 9 tissue types (lung, colon, ovary, prostate, blood, breast, testis, kidney, and muscle). We identified 6,350 smoking-associated CpGs in lung tissue (n = 212) and 2,735 in colon tissue (n = 210), most not reported previously. For all 7 other tissue types (sample sizes 38-153), no clear associations were observed (false discovery rate 0.05), but some tissues showed enrichment for smoking-associated CpGs reported previously. For 1,646 loci (in lung) and 22 (in colon), smoking was associated with both DNAm and local gene expression. For loci detected in both lung and colon (e.g., AHRR, CYP1B1, CYP1A1), top CpGs often differed between tissues, but similar clusters of hyper- or hypomethylated CpGs were observed, with hypomethylation at regulatory elements corresponding to increased expression. For lung tissue, 17 hallmark gene sets were enriched for smoking-associated CpGs, including xenobiotic- and cancer-related gene sets. At least four smoking-associated regions in lung were impacted by lung methylation quantitative trait loci (QTLs) that co-localize with genome-wide association study (GWAS) signals for lung function (FEV1/FVC), suggesting epigenetic alterations can mediate the effects of smoking on lung health. Our multi-tissue approach has identified smoking-associated regions in disease-relevant tissues, including effects that are shared across tissue types.

miRNAs from Inflamed Gingiva Link Gene Signaling to Increased MET Expression

Several array-based microRNA (miRNA) expression studies independently showed increased expression of miRNAs hsa-miR-130a-3p, -142-3p, -144-3p, -144-5p, -223-3p, -17-5p, and -30e-5p in gingiva affected by periodontal inflammation. We aimed to determine direct target genes and signaling pathways regulated by these miRNAs to identify processes relevant to gingival inflammatory responses and tissue homeostasis. We transfected miRNA mimics (mirVana) for each of the 7 miRNAs separately into human primary gingival fibroblasts cultured from 3 different donors. Following RNA sequencing, differential gene expression and second-generation gene set enrichment analyses were performed. miRNA inhibition and upregulation was validated at the transcript and protein levels using quantitative reverse transcriptase polymerase chain reaction, Western blotting, and reporter gene assays. All 7 miRNAs significantly increased expression of the gene MET proto-oncogene, receptor tyrosine kinase (MET). Expression of known periodontitis risk genes CPEB1, ABCA1, and ATP6V1C1 was significantly repressed by hsa-miR-130a-3p, -144-3p, and -144-5p, respectively. The genes WASL, ENPP5, ARL6IP1, and IDH1 showed the most significant and strongest downregulation after hsa-miR-142-3p, -17-5p, -223-3p, and -30e-5p transfection, respectively. The most significantly regulated gene set of each miRNA related to cell cycle (hsa-miRNA-144-3p and -5p [Padj = 4 × 10-40 and Padj = 4 × 10-6], -miR-17-5p [Padj = 9.5 × 10-23], -miR-30e-5p [Padj = 8.2 × 10-18], -miR-130a-3p [Padj = 5 × 10-15]), integrin cell surface interaction (-miR-223-3p [Padj = 2.4 × 10-7]), and interferon signaling (-miR-142-3p [Padj = 5 × 10-11]). At the end of acute inflammation, gingival miRNAs bring together complex regulatory networks that lead to increased expression of the gene MET. This underscores the importance of mesenchymal cell migration and invasion during gingival tissue remodeling and proliferation in restoring periodontal tissue homeostasis after active inflammation. MET, a receptor of the mitogenic hepatocyte growth factor fibroblast secreted, is a core gene of this process.

Non-small Cell Lung Cancer Epigenomes Exhibit Altered DNA Methylation in Smokers and Never-smokers

Epigenetic alterations are widespread in cancer and can complement genetic alterations to influence cancer progression and treatment outcome. To determine the potential contribution of DNAmethylation alterations to tumor phenotype in non-small cell lung cancer (NSCLC) in both smoker and never-smoker patients, we performed genome-wide profiling of DNA methylation in 17 primary NSCLC tumors and 10 matched normal lung samples using the complementary assays, methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation sensitive restriction enzyme sequencing (MRE-seq). We reported recurrent methylation changes in the promoters of several genes, many previously implicated in cancer, including FAM83A and SEPT9 (hypomethylation), as well as PCDH7, NKX2-1, and SOX17 (hypermethylation). Although many methylation changes between tumors and their paired normal samples were shared across patients, several were specific to a particular smoking status. For example, never-smokers displayed a greater proportion of hypomethylated differentially methylated regions (hypoDMRs) and a greater number of recurrently hypomethylated promoters, including those of ASPSCR1, TOP2A, DPP9, and USP39, all previously linked to cancer. Changes outside of promoters were also widespread and often recurrent, particularly methylation loss over repetitive elements, highly enriched for ERV1 subfamilies. Recurrent hypoDMRs were enriched for several transcription factor binding motifs, often for genes involved in signaling and cell proliferation. For example, 71% of recurrent promoter hypoDMRs contained a motif for NKX2-1. Finally, the majority of DMRs were located within an active chromatin state in tissues profiled using the Roadmap Epigenomics data, suggesting that methylation changes may contribute to altered regulatory programs through the adaptation of cell type-specific expression programs.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Aberrant AHRR, ADAMTS2 and FAM184 DNA Methylation: Candidate Biomarkers in the Oral Rinse of Heavy Smokers

OBJECTIVE

To identify DNA methylation patterns of heavy smokers in oral rinse samples.

METHODS

Genome-wide DNA methylation data was imported from Gene Expression Omnibus GSE70977 using the GEOquery package. Two independent sets were analyzed: (a) 71 epigenomes of cancer-free subjects (heavy smokers n = 37 vs. non-smokers n = 31); for concordance assessment (b) 139 oral-cancer patients' epigenomes (heavy smokers n = 92 vs. non-smokers n = 47). Differential DNA methylation for CpG positions and at the regional level was determined using Limma and DMRcate Bioconductor packages. The linear model included sex, age, and alcohol consumption. The statistical threshold was set to p < 0.05. Functional gene prioritization analysis was performed for gene-targeted analysis.

RESULTS

In individuals without cancer and heavy smokers, the FAM184B gene was found with two CpG positions differentially hypermethylated (p = 0.012 after FDR adjustment), in a region of 48 bp with an absolute methylation difference >10% between groups (p = 1.76 × 10^-8). In the analysis corresponding to oral-cancer patients, we found AHRR differentially hypomethylated cancer patients, but also in subjects without oral cancer in the targeted analyses. Remarkably, ADAMTS2 was found differentially hypermethylated in heavy smokers without a diagnosis of cancer in two consecutive probes cg05575921 (p = 3.13 × 10^-7) and cg10208897 (p = 1.36 × 10^-5).

CONCLUSIONS

Differentially methylated AHRR, ADAMTS2, and FAM184B genes are biomarker candidates in oral rinse samples.

Transcriptomic analysis predicts the risk of progression of premalignant lesions in human tongue

The 5-year survival rate for patients with oral squamous cell carcinomas (SCC), including tongue SCC, has not significantly improved over the last several decades. Oral potentially malignant disorders (OPMD), including oral dysplasias, are oral epithelial disorders that can develop into oral SCCs. To identify molecular characteristics that might predict conversion of OPMDs to SCCs and guide treatment plans, we performed global transcriptomic analysis of human tongue OPMD (n = 9) and tongue SCC (n = 11) samples with paired normal margin tissue from patients treated at Weill Cornell Medicine. Compared to margin tissue, SCCs showed more transcript changes than OPMDs. OPMDs and SCCs shared some altered transcripts, but these changes were generally greater in SCCs than OPMDs. Both OPMDs and SCCs showed altered signaling pathways related to cell migration, basement membrane disruption, and metastasis. We suggest that OPMDs are on the path toward malignant transformation. Based on patterns of gene expression, both OPMD and tongue SCC samples can be categorized into subclasses (mesenchymal, classical, basal, and atypical) similar to those seen in human head and neck SCC (HNSCC). These subclasses of OPMDs have the potential to be used to stratify patient prognoses and therapeutic options for tongue OPMDs. Lastly, we identified a gene set (ELF5; RPTN; IGSF10; CRMP1; HTR3A) whose transcript changes have the power to classify OPMDs and SCCs and developed a Firth logistic regression model using the changes in these transcripts relative to paired normal tissue to validate pathological diagnosis and potentially predict the likelihood of an OPMD developing into SCC, as data sets become available.

Epigenetics in susceptibility, progression, and diagnosis of periodontitis

Periodontitis is characterized by irreversible destruction of periodontal tissue. At present, the accepted etiology of periodontitis is based on a three-factor theory including pathogenic bacteria, host factors, and acquired factors. Periodontitis development usually takes a decade or longer and is therefore called chronic periodontitis (CP). To search for genetic factors associated with CP, several genome-wide association study (GWAS) analyses were conducted; however, polymorphisms associated with CP have not been identified. Epigenetics, on the other hand, involves acquired transcriptional regulatory mechanisms due to reversibly altered chromatin accessibility. Epigenetic status is a condition specific to each tissue and cell, mostly determined by the responses of host cells to stimulations by local factors, like bacterial inflammation, and systemic factors such as nutrition status, metabolic diseases, and health conditions. Significantly, epigenetic status has been linked with the onset and progression of several acquired diseases. Thus, epigenetic factors in periodontal tissues are attractive targets for periodontitis diagnosis and treatments. In this review, we introduce accumulating evidence to reveal the epigenetic background effects related to periodontitis caused by genetic factors, systemic diseases, and local environmental factors, such as smoking, and clarify the underlying mechanisms by which epigenetic alteration influences the susceptibility of periodontitis.

Preliminary study on the molecular features of mutation in multiple primary oral cancer by whole exome sequencing

Multiple primary cancers (MPCs) refer to cancers that occur simultaneously or metachronously in the same individual. The incidence of MPC has increased recently, as the survival time of malignant tumor patients has been greatly prolonged. It is difficult to differentiate MPC from primary cancers (PCs) in the same anatomical region from the clinical manifestation alone. However, their biological behaviors appear to be distinct. In this study, we show that the prognosis of multiple primary oral cancers (MP-OCs) is worse than primary oral cancers (P-OCs). To better understand the molecular mechanisms of MP-OC, we used whole exome sequencing (WES) to analyze samples from 9 patients with MP-OC and 21 patients with P-OC. We found more somatic mutations in MP-OC than in P-OC. MP-OC had more complicated mutation signatures, which were associated with age-related and Apolipoprotein B mRNA Editing Catalytic Polypeptide-like (APOBEC) activity-related signatures. Tumor mutational burden (TMB) and mutant-allele tumor heterogeneity (MATH) of MP-OC trended higher compared to P-OC. KEGG and GO analysis showed the differential pathways of MP-OC versus P-OC. In addition, MP-OC took amplification, not loss, as the main pattern of copy number variation (CNV), while P-OC took both. Lastly, we did not find significantly different mutant germline genes, but MSH-6 mutation may be a potential MP-OC driver. In short, our preliminary results show that MP-OC and P-OC have different molecular characteristics.

RNA-Seq Analysis of UPM-Exposed Epithelium Co-Cultivated with Macrophages and Dendritic Cells in Obstructive Lung Diseases

Background. Elevated concentrations of airborne pollutants are correlated with an enlarged rate of obstructive lung disease morbidity as well as acute disease exacerbations. This study aimed to analyze the epithelium mRNA profile in response to airborne particulate matter in the control, asthma, and COPD groups. Results. A triple co-culture of nasal epithelium, monocyte-derived macrophages, and monocyte-derived dendritic cells obtained from the controls, asthma, and COPD were exposed to urban particulate matter (UPM) for 24 h. RNA-Seq analysis found differences in seven (CYP1B1, CYP1B1-AS1, NCF1, ME1, LINC02029, BPIFA2, EEF1A2), five (CYP1B1, ARC, ENPEP, RASD1, CYP1B1-AS1), and six (CYP1B1, CYP1B1-AS1, IRF4, ATP1B2, TIPARP, CCL22) differentially expressed genes between UPM exposed and unexposed triple co-cultured epithelium in the control, asthma, and COPD groups, respectively. PCR analysis showed that mRNA expression of BPIFA2 and ENPEP was upregulated in both asthma and COPD, while the expression of CYP1B1-AS1 and TIPARP was increased in the epithelium from COPD patients only. Biological processes changed in UPM exposed triple co-cultured epithelium were associated with epidermis development and epidermal cell differentiation in asthma and with response to toxic substances in COPD. Conclusions. The biochemical processes associated with pathophysiology of asthma and COPD impairs the airway epithelial response to UPM.

Identification of nine signature proteins involved in periodontitis by integrated analysis of TMT proteomics and transcriptomics

Recently, there are many researches on signature molecules of periodontitis derived from different periodontal tissues to determine the disease occurrence and development, and deepen the understanding of this complex disease. Among them, a variety of omics techniques have been utilized to analyze periodontitis pathology and progression. However, few accurate signature molecules are known and available. Herein, we aimed to screened and identified signature molecules suitable for distinguishing periodontitis patients using machine learning models by integrated analysis of TMT proteomics and transcriptomics with the purpose of finding novel prediction or diagnosis targets. Differential protein profiles, functional enrichment analysis, and protein–protein interaction network analysis were conducted based on TMT proteomics of 15 gingival tissues from healthy and periodontitis patients. DEPs correlating with periodontitis were screened using LASSO regression. We constructed a new diagnostic model using an artificial neural network (ANN) and verified its efficacy based on periodontitis transcriptomics datasets (GSE10334 and GSE16134). Western blotting validated expression levels of hub DEPs. TMT proteomics revealed 5658 proteins and 115 DEPs, and the 115 DEPs are closely related to inflammation and immune activity. Nine hub DEPs were screened by LASSO, and the ANN model distinguished healthy from periodontitis patients. The model showed satisfactory classification ability for both training (AUC=0.972) and validation (AUC=0.881) cohorts by ROC analysis. Expression levels of the 9 hub DEPs were validated and consistent with TMT proteomics quantitation. Our work reveals that nine hub DEPs in gingival tissues are closely related to the occurrence and progression of periodontitis and are potential signature molecules involved in periodontitis.

Cellular expression of epigenetic markers and oxidative stress in periodontitis lesions of smokers and non-smokers

Objective

To evaluate differences in the cellular expression of epigenetic markers and oxidative stress in periodontitis lesions between current smokers and non‐smokers.

Background

Tobacco smoking is recognized as one of the major risk factors for periodontitis. However, the mechanisms by which smoking affects the progression of the disease remain to be determined.

Methods

Twenty‐five current smokers and 21 non‐smokers with generalized severe periodontitis were included. From each patient, one soft tissue biopsy from a periodontitis site was harvested and prepared for histological analysis. The infiltrated connective tissue (ICT) was selected as the region of interest to assess the cellular expression of epigenetic markers and reactive oxygen/nitrogen species (RONS) by immunohistochemistry.

Results

Although the ICT of smokers and non‐smokers did not differ in size or in the expression of markers for DNA damage or oxidative stress, current smokers presented with significantly lower area proportions and densities of cells positive for the epigenetic markers DNMT1 and AcH3. In addition, periodontitis lesions in current smokers presented with a diminished antimicrobial activity, as indicated by significantly lower densities and area proportions of NOX2‐ and iNOS‐positive cells.

Conclusions

Components of the host response and epigenetic mechanisms in periodontitis lesions in smokers are downregulated as opposed to lesions of non‐smokers.

Genome-wide evaluation of transcriptomic responses of human tissues to smoke: A systems biology study

The harmful compounds in various sources of smoke threaten human health. So far, many studies have investigated the effects of compounds of smoke on transcriptome changes in different human tissues. However, no study has been conducted on the effects of these compounds on transcriptome changes in different human tissues simultaneously. Hence, the present study was conducted to identify smoke-related genes (SRGs) and their response mechanisms to smoke in various human cells and tissues using systems biology based methods. A total of 6,484 SRGs were identified in the studied tissues, among which 4,095 SRGs were up-regulated and 2,389 SRGs were down-regulated. Totally, 459 SRGs were smoke-related transcription factors (SRTFs). Gene regulatory network analysis showed that the studied cells and tissues have different gene regulation and responses to compounds of smoke. The comparison of different tissues revealed no common SRG among the all studied tissues. However, the CYP1B1 gene was common among seven cells and tissues, and had the same expression trend. Network analysis showed that the CYP1B1 is a hub gene among SRGs in various cells and tissues. To the best of our knowledge, for the first time, our results showed that compounds of smoke induce and increase the expression of CYP1B1 key gene in all target and non-target tissues of human. Moreover, despite the specific characteristics of CYP1B1 gene and its identical expression trend in target and non-target tissues, it can be used as a biomarker for diagnosis and prognosis.

Epigenetic adaptations of the masticatory mucosa to periodontal inflammation

Background

In mucosal barrier interfaces, flexible responses of gene expression to long-term environmental changes allow adaptation and fine-tuning for the balance of host defense and uncontrolled not-resolving inflammation. Epigenetic modifications of the chromatin confer plasticity to the genetic information and give insight into how tissues use the genetic information to adapt to environmental factors. The oral mucosa is particularly exposed to environmental stressors such as a variable microbiota. Likewise, persistent oral inflammation is the most important intrinsic risk factor for the oral inflammatory disease periodontitis and has strong potential to alter DNA-methylation patterns. The aim of the current study was to identify epigenetic changes of the oral masticatory mucosa in response to long-term inflammation that resulted in periodontitis.

Methods and results

Genome-wide CpG methylation of both inflamed and clinically uninflamed solid gingival tissue biopsies of 60 periodontitis cases was analyzed using the Infinium MethylationEPIC BeadChip. We validated and performed cell-type deconvolution for infiltrated immune cells using the EpiDish algorithm. Effect sizes of DMPs in gingival epithelial and fibroblast cells were estimated and adjusted for confounding factors using our recently developed “intercept-method”. In the current EWAS, we identified various genes that showed significantly different methylation between periodontitis-inflamed and uninflamed oral mucosa in periodontitis patients. The strongest differences were observed for genes with roles in wound healing (ROBO2, PTP4A3), cell adhesion (LPXN) and innate immune response (CCL26, DNAJC1, BPI). Enrichment analyses implied a role of epigenetic changes for vesicle trafficking gene sets.

Conclusions

Our results imply specific adaptations of the oral mucosa to a persistent inflammatory environment that involve wound repair, barrier integrity, and innate immune defense.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01190-7.

Differentially methylated genes in proliferative verrucous leukoplakia reveal potential malignant biomarkers for oral squamous cell carcinoma

OBJECTIVES

To explore the pathophysiology of proliferative verrucous leucoplakia (PVL) through a methylated DNA immunoprecipitation and high-throughput sequencing (MeDIP-seq) case-control study.

MATERIALS AND METHODS

Oral biopsies from ten PVL patients and five healthy individuals were obtained and used to compare their epigenetic patterns. Network biology methods and integrative analyses of MeDIP-seq and RNAseq data were applied to investigate functional relations among differentially methylated genes (DMGs). The value of selected genes as malignant biomarkers was evaluated in a large cohort of oral squamous cell carcinoma (OSCC) patients from TCGA.

RESULTS

A total of 4647 differentially methylated regions were found, with a prominent state of hypermethylation in PVL patients. At the gene level, differentially methylated regions (DMRs) covered 826 genes with distinct roles, including transcription factors and binding proteins with functions in cell adhesion, migration, proliferation, regulation of transcription, bone morphogenesis, and cell signalling. Network analysis revealed three major hubs, two of them collecting proteins related to the response of the patients to PVL and treatment and one hub collecting proteins related to PVL and cancer. The integrative analysis revealed 8 genes (ARTN, CD8A, GATA3, HOXD10, MYO7A, OSR2, PLCB1, and SPOCK2) significantly upregulated in PVL compared to control and 5 genes (ANKRD6, DLG2, GPX3, PITX2, and ZNF736) significantly downregulated. The status of de-regulation found for PVL patients was concordant with what was found for OSCC samples compared to normal adjacent tissue.

CONCLUSION

Our findings show the potential of methylation markers in PVL and suggest novel OSCC diagnostic biomarkers which may boost the development of novel epigenetic-based therapies.

Expression Pattern of the SARS-CoV-2 Entry Genes ACE2 and TMPRSS2 in the Respiratory Tract

To address the expression pattern of the SARS-CoV-2 receptor ACE2 and the viral priming protease TMPRSS2 in the respiratory tract, this study investigated RNA sequencing transcriptome profiling of samples of airway and oral mucosa. As shown, ACE2 has medium levels of expression in both small airway epithelium and masticatory mucosa, and high levels of expression in nasal epithelium. The expression of ACE2 is low in mucosal-associated invariant T (MAIT) cells and cannot be detected in alveolar macrophages. TMPRSS2 is highly expressed in small airway epithelium and nasal epithelium and has lower expression in masticatory mucosa. Our results provide the molecular basis that the nasal mucosa is the most susceptible locus in the respiratory tract for SARS-CoV-2 infection and consequently for subsequent droplet transmission and should be the focus for protection against SARS-CoV-2 infection.

Expression Pattern of the SARS-CoV-2 Entry Genes ACE2 and TMPRSS2 in the Respiratory Tract

To address the expression pattern of the SARS-CoV-2 receptor ACE2 and the viral priming protease TMPRSS2 in the respiratory tract, this study investigated RNA sequencing transcriptome profiling of samples of airway and oral mucosa. As shown, ACE2 has medium levels of expression in both small airway epithelium and masticatory mucosa, and high levels of expression in nasal epithelium. The expression of ACE2 is low in mucosal-associated invariant T (MAIT) cells and cannot be detected in alveolar macrophages. TMPRSS2 is highly expressed in small airway epithelium and nasal epithelium and has lower expression in masticatory mucosa. Our results provide the molecular basis that the nasal mucosa is the most susceptible locus in the respiratory tract for SARS-CoV-2 infection and consequently for subsequent droplet transmission and should be the focus for protection against SARS-CoV-2 infection.

Detection of suspicious interactions of spiking covariates in methylation data

BACKGROUND

In methylation analyses like epigenome-wide association studies, a high amount of biomarkers is tested for an association between the measured continuous outcome and different covariates. In the case of a continuous covariate like smoking pack years (SPY), a measure of lifetime exposure to tobacco toxins, a spike at zero can occur. Hence, all non-smokers are generating a peak at zero, while the smoking patients are distributed over the other SPY values. Additionally, the spike might also occur on the right side of the covariate distribution, if a category "heavy smoker" is designed. Here, we will focus on methylation data with a spike at the left or the right of the distribution of a continuous covariate. After the methylation data is generated, analysis is usually performed by preprocessing, quality control, and determination of differentially methylated sites, often performed in pipeline fashion. Hence, the data is processed in a string of methods, which are available in one software package. The pipelines can distinguish between categorical covariates, i.e. for group comparisons or continuous covariates, i.e. for linear regression. The differential methylation analysis is often done internally by a linear regression without checking its inherent assumptions. A spike in the continuous covariate is ignored and can cause biased results.

RESULTS

We have reanalysed five data sets, four freely available from ArrayExpress, including methylation data and smoking habits reported by smoking pack years. Therefore, we generated an algorithm to check for the occurrences of suspicious interactions between the values associated with the spike position and the non-spike positions of the covariate. Our algorithm helps to decide if a suspicious interaction can be found and further investigations should be carried out. This is mostly important, because the information on the differentially methylated sites will be used for post-hoc analyses like pathway analyses.

CONCLUSIONS

We help to check for the validation of the linear regression assumptions in a methylation analysis pipeline. These assumptions should also be considered for machine learning approaches. In addition, we are able to detect outliers in the continuous covariate. Therefore, more statistical robust results should be produced in methylation analysis using our algorithm as a preprocessing step.

Smoking Modifies the Genetic Risk for Early-Onset Periodontitis

Periodontitis has low-prevalence, highly severe disease manifestations with an early onset and rapid progression. The diagnosis is based on severe destruction of the alveolar bone in adolescents and young adults. Genetic susceptibility variants and smoking are well-established risk factors, but their interactions in modifying disease susceptibility have not been studied. We aimed to identify genetic risk variants of early-onset periodontitis that unmask their effects on tobacco smoke exposure. To this end, we analyzed 79,780,573 common variants in 741 northwest Europeans diagnosed to have >30% bone loss at >2 teeth before 35 y of age, using imputed genotypes of the OmniExpress BeadChip. Never versus ever smokers were compared in a logistic regression analysis via a case-only approach. To explore the effect of tobacco smoke on the expression of the G×S-associated genes, cultures of primary gingival fibroblasts ( n = 9) were exposed to cigarette smoke extract, and transcripts were quantified by reverse transcription polymerase chain reaction. We identified 16 loci for which our analysis suggested an association with G×S increased disease risk ( P < 5 × 10−5). Nine loci had previously been reported to be associated with spirometric measures of pulmonary function by an earlier G×S genome-wide association study. Genome-wide significant cis expression quantitative trait loci were reported for G×S-associated single-nucleotide polymorphisms at ST8SIA1 and SOST, indicating a causal role of these genes in tobacco-related etiopathology. Notably, SOST is a negative regulator of bone growth, and ST8SIA1 has a role in tissue remodeling. Cigarette smoke extract significantly altered the expression of 2 associated genes: SSH1 ( P = 5 × 10−07), which is required for NF-κB activation and innate immune responses to bacterial invasion, and ST8SIA1 ( P = 0.0048). We conclude that the genetic predisposition to early-onset periodontitis is in part triggered by smoking and that tobacco smoke directly affects the expression of genes involved in bone homeostasis, tissue repair, and immune response.