Analysis of protein expression in periodontal pocket tissue: a preliminary study

Could the periodontal therapy improve the cardiologic patient health? A narrative review

BACKGROUND

Cardiovascular diseases (CVD) is the major cause of mortality globally, with increasing evidence suggesting a link between periodontitis, and CVD. This study aims to explore the association between periodontitis and CVD, and the impact of periodontal therapy on cardiovascular health.

METHODS

This review synthesized findings from preclinical and clinical studies, without publication year restrictions, examining periodontitis and CVD through various lenses. Scientific databases were inspected with keywords related to periodontitis and CVD.

RESULTS

The review identifies a substantial association between periodontitis and an increased risk of several CVD, supported by both epidemiological and interventional studies. Results suggest the complexity of the relationship, influenced by factors like the severity of periodontitis and the presence of other systemic conditions. Clinical data indicates that periodontal therapy, particularly non-surgical periodontal therapy, may reduce systemic inflammatory markers and thus may play a role in the primary and secondary prevention of CVD events, highlighting the potential of periodontal therapy to not only maintain oral health but also to modulate cardiovascular risk factors.

CONCLUSIONS

Current evidence supports a significant association between periodontitis and increased cardiovascular risk, promoting integrated healthcare approaches that consider oral health as a key-component of cardiovascular care and wellbeing.

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.

Proteomic Analysis in Nifedipine Induced Gingival Overgrowth: A Pilot Study

Objective: The aims of the present study were to investigate the proteomic profile of nifedipine induced overgrown gingiva and compare with non-overgrown gingival tissues obtained from the same patients. Methods: Seven subjects under nifedipine medication for at least 6 months and diagnosed as nifedipine induced gingival overgrowth (NIGO) participated in the study. Periodontal clinical parameters were recorded. Gingival tissue samples were harvested from overgrown (GO+ Group, n=7) and non-overgrown regions (GO- Group, n=7) of the same patients. Proteomics was performed using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) technique. The identified proteins were further classified according to their molecular functions, biological processes and cellular component distribution for functional gene ontology analysis using a web-based bioinformatics tool. Mann Whitney-U and ANOVA tests were performed to compare clinical parameters and identified proteins with proteomics, respectively. Results: Bleeding on probing and gingival overgrowth index of the GO+ group were statistically significantly higher than the GO- group (p

Characterization of Acidic Mammalian Chitinase as a Novel Biomarker for Severe Periodontitis (Stage III/IV): A Pilot Study

Periodontitis is a chronic inflammatory condition characterized by gingival infection, periodontal pocket formation, and alveolar bone loss. Acidic mammalian chitinase (AMCase), an active chitinase enzyme, increased its expression under severe inflammation and related systemic disorders. However, AMCase expression and molecular mechanism in periodontal inflammation, have not been elucidated yet. This study was aimed to characterize AMCase in severe periodontitis patients compare to those in periodontally healthy subjects. In total, 15 periodontally healthy subjects and 15 severe (stage III/IV) periodontitis patients were enrolled with their informed consent. Tissue samples were collected and analyzed using Western blot and enzyme-linked immunosorbent assay (ELISA). AMCase protein expressions in periodontal patients were significantly more increased than those of periodontally healthy individuals. ELISA resulted in median values (first quartile to third quartile) of the periodontally healthy group 0.654 ng/mL (range, 0.644−0.827 ng/mL) and the periodontitis group 0.965 ng/mL (range, 0.886−1.165 ng/mL). AMCase was expressed significantly higher levels in periodontitis patients than in periodontally healthy individuals (p < 0.05). This suggests that AMCase may play a potential role as a biomarker for the screening and early diagnosis of severe periodontitis.

Proteomics Disclose the Potential of Gingival Crevicular Fluid (GCF) as a Source of Biomarkers for Severe Periodontitis

Periodontal disease is a widespread disorder comprising gingivitis, a mild early gum inflammation, and periodontitis, a more severe multifactorial inflammatory disease that, if left untreated, can lead to the gradual destruction of the tooth-supporting apparatus. To date, effective etiopathogenetic models fully explaining the clinical features of periodontal disease are not available. Obviously, a better understanding of periodontal disease could facilitate its diagnosis and improve its treatment. The purpose of this study was to employ a proteomic approach to analyze the gingival crevicular fluid (GCF) of patients with severe periodontitis, in search of potential biomarkers. GCF samples, collected from both periodontally healthy sites (H-GCF) and the periodontal pocket (D-GCF), were subjected to a comparison analysis using sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). A total of 26 significantly different proteins, 14 up-regulated and 12 down-regulated in D-GCF vs. H-GCF, were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The main expressed proteins were inflammatory molecules, immune responders, and host enzymes. Most of these proteins were functionally connected using the STRING analysis database. Once validated in a large scale-study, these proteins could represent a cluster of promising biomarkers capable of making a valuable contribution for a better assessment of periodontitis.

NETs Are Double-Edged Swords with the Potential to Aggravate or Resolve Periodontal Inflammation

Periodontitis is a general term for diseases characterised by inflammatory destruction of tooth-supporting tissues, gradual destruction of the marginal periodontal ligament and resorption of alveolar bone. Early-onset periodontitis is due to disturbed neutrophil extracellular trap (NET) formation and clearance. Indeed, mutations that inactivate the cysteine proteases cathepsin C result in the massive periodontal damage seen in patients with deficient NET formation. In contrast, exaggerated NET formation due to polymorphonuclear neutrophil (PMN) hyper-responsiveness drives the pathology of late-onset periodontitis by damaging and ulcerating the gingival epithelium and retarding epithelial healing. Despite the gingival regeneration, periodontitis progression ends with almost complete loss of the periodontal ligament and subsequent tooth loss. Thus, NETs help to maintain periodontal health, and their dysregulation, either insufficiency or surplus, causes heavy periodontal pathology and edentulism.

Proteome and Microbiome Mapping of Human Gingival Tissue in Health and Disease

Efforts to map gingival tissue proteomes and microbiomes have been hampered by lack of sufficient tissue extraction methods. The pressure cycling technology (PCT) is an emerging platform for reproducible tissue homogenisation and improved sequence retrieval coverage. Therefore, we employed PCT to characterise the proteome and microbiome profiles in healthy and diseased gingival tissue. Healthy and diseased contralateral gingival tissue samples (total n = 10) were collected from five systemically healthy individuals (51.6 ± 4.3 years) with generalised chronic periodontitis. The tissues were then lysed and digested using a Barocycler, proteins were prepared and submitted for mass spectrometric analysis and microbiome DNA for 16S rRNA profiling analysis. Overall, 1,366 human proteins were quantified (false discovery rate 0.22%), of which 69 proteins were differentially expressed (≥2 peptides and p < 0.05, 62 up, 7 down) in periodontally diseased sites, compared to healthy sites. These were primarily extracellular or vesicle-associated proteins, with functions in molecular transport. On the microbiome level, 362 species-level operational taxonomic units were identified. Of those, 14 predominant species accounted for >80% of the total relative abundance, whereas 11 proved to be significantly different between healthy and diseased sites. Among them, Treponema sp. HMT253 and Fusobacterium naviforme and were associated with disease sites and strongly interacted (r > 0.7) with 30 and 6 up-regulated proteins, respectively. Healthy-site associated strains Streptococcus vestibularis, Veillonella dispar, Selenomonas sp. HMT478 and Leptotrichia sp. HMT417 showed strong negative interactions (r < −0.7) with 31, 21, 9, and 18 up-regulated proteins, respectively. In contrast the down-regulated proteins did not show strong interactions with the regulated bacteria. The present study identified the proteomic and intra-tissue microbiome profile of human gingiva by employing a PCT-assisted workflow. This is the first report demonstrating the feasibility to analyse full proteome profiles of gingival tissues in both healthy and disease sites, while deciphering the tissue site-specific microbiome signatures.

Proteomics approach for biomarkers and diagnosis of periodontitis: systematic review

Quantitative proteomic workflow based on mass spectrometry (MS) is recently developed by the researchers to screen for biomarkers in periodontal diseases comprising periodontitis. Periodontitis is known for chronic inflammatory disease characterized by progressive destruction of the tooth-supporting apparatus, yet has a lack of clear pathobiology based on a discrepancy between specified categories and diagnostic vagueness. The objective of this review was to outlined the accessible information related to proteomics studies on periodontitis. The Preferred Reporting Items for Systematical Reviews and Meta-Analysis (PRISMA) statement guides to acquaint proteomic analysis on periodontal diseases was applied. Three databases were used in this study, such as Pubmed, ScienceDirect and Biomed Central from 2009 up to November 2019. Proteomics analysis platforms that used in the studies were outlined. Upregulated and downregulated proteins findings data were found, in which could be suitable as candidate biomarkers for this disease.

An Evidence-Based Update on the Molecular Mechanisms Underlying Periodontal Diseases

Several investigators have reported about the intricate molecular mechanism underlying periodontal diseases (PD). Nevertheless, the role of specific genes, cells, or cellular mechanisms involved in the pathogenesis of periodontitis are still unclear. Although periodontitis is one of the most prevalent oral diseases globally, there are no pre-diagnostic markers or therapeutic targets available for such inflammatory lesions. A pivotal role is played by pro- and anti-inflammatory markers in modulating pathophysiological and physiological processes in repairing damaged tissues. In addition, effects on osteoimmunology is ever evolving due to the ongoing research in understanding the molecular mechanism lying beneath periodontal diseases. The aim of the current review is to deliver an evidence-based update on the molecular mechanism of periodontitis with a particular focus on recent developments. Reports regarding the molecular mechanism of these diseases have revealed unforeseen results indicative of the fact that significant advances have been made to the periodontal medicine over the past decade. There is integrated hypothesis-driven research going on. Although a wide picture of association of periodontal diseases with immune response has been further clarified with present ongoing research, small parts of the puzzle remain a mystery and require further investigations.

Host-microbe interactions: Profiles in the transcriptome, the proteome, and the metabolome

Periodontal studies using transcriptomics, proteomics, and metabolomics encompass the collection of mRNA transcripts, proteins, and small-molecule chemicals in the context of periodontal health and disease. The number of studies using these approaches has significantly increased in the last decade and they have provided new insight into the pathogenesis and host-microbe interactions that define periodontal diseases. This review provides an overview of current molecular findings using -omic approaches that underlie periodontal disease, including modulation of the host immune response, tissue homeostasis, and complex metabolic processes of the host and the oral microbiome. Integration of these -omic approaches will broaden our perspective of the molecular mechanisms involved in periodontal disease, advancing and improving the diagnosis and treatment of various stages and forms of periodontal disease.

Pressure Cycling Technology Assisted Mass Spectrometric Quantification of Gingival Tissue Reveals Proteome Dynamics during the Initiation and Progression of Inflammatory Periodontal Disease

Understanding the progression of periodontal tissue destruction is at the forefront of periodontal research. The authors aimed to capture the dynamics of gingival tissue proteome during the initiation and progression of experimental (ligature-induced) periodontitis in mice. Pressure cycling technology (PCT), a recently developed platform that uses ultra-high pressure to disrupt tissues, is utilized to achieve efficient and reproducible protein extraction from ultra-small amounts of gingival tissues in combination with liquid chromatography-tandem mass spectrometry (MS). The MS data are processed using Progenesis QI and the regulated proteins are subjected to METACORE, STRING, and WebGestalt for functional enrichment analysis. A total of 1614 proteins with ≥2 peptides are quantified with an estimated protein false discovery rate of 0.06%. Unsupervised clustering analysis shows that the gingival tissue protein abundance is mainly dependent on the periodontitis progression stage. Gene ontology enrichment analysis reveals an overrepresentation in innate immune regulation (e.g., neutrophil-mediated immunity and antimicrobial peptides), signal transduction (e.g., integrin signaling), and homeostasis processes (e.g., platelet activation and aggregation). In conclusion, a PCT-assisted label-free quantitative proteomics workflow that allowed cataloging the deepest gingival tissue proteome on a rapid timescale and provided novel mechanistic insights into host perturbation during periodontitis progression is applied.