High-throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis

Using SWATH-MS to identify new molecular biomarkers in gingival crevicular fluid for detecting periodontitis and its response to treatment

AIM

To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS).

MATERIALS AND METHODS

GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions.

RESULTS

In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions.

CONCLUSIONS

New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.

Progression from healthy periodontium to gingivitis and periodontitis: Insights from bioinformatics-driven proteomics - A systematic review with meta-analysis

AIM

The current study aimed to: (1) systematically review the published literature regarding the proteomics analyses of saliva and gingival crevicular fluid (GCF) in healthy humans and gingivitis and/or periodontitis patients; and (2) to identify the differentially expressed proteins (DEPs) based on the systematic review, and comprehensively conduct meta-analyses and bioinformatics analyses.

METHODS

An online search of Web of Science, Scopus, and PubMed was performed without any restriction on the year and language of publication. After the identification of the DEPs reported by the included human primary studies, gene ontology (GO), the Kyoto encyclopedia of genes and genomes pathway (KEGG), protein-protein interaction (PPI), and meta-analyses were conducted. The risk of bias among the included studies was evaluated using the modified Newcastle-Ottawa quality assessment scale.

RESULTS

The review identified significant differences in protein expression between healthy individuals and those with gingivitis and periodontitis. In GCF, 247 proteins were upregulated and 128 downregulated in periodontal diseases. Saliva analysis revealed 79 upregulated and 70 downregulated proteins. There were distinct protein profiles between gingivitis and periodontitis, with 159 and 31 unique upregulated proteins in GCF, respectively. Meta-analyses confirmed significant upregulation of various proteins in periodontitis, including ALB and MMP9, while CSTB and GSTP1 were downregulated. AMY1A and SERPINA1 were upregulated in periodontitis saliva. HBD was upregulated in gingivitis GCF, while DEFA3 was downregulated. PPI analysis revealed complex networks of interactions among DEPs. GO and KEGG pathway analyses provided insights into biological processes and pathways associated with periodontal diseases.

CONCLUSION

The ongoing MS-based proteomics studies emphasize the need for a highly sensitive and specific diagnostic tool for periodontal diseases. Clinician acceptance of the eventual diagnostic method relies on its ability to provide superior or complementary information to current clinical assessment procedures. Future research should prioritize the multiplex measurement of multiple biomarkers simultaneously to enhance diagnostic accuracy and large study cohorts are necessary to ensure the validity and reliability of research findings.

Diagnostic value of aMMP-8 and azurocidin in peri-implant sulcular fluid as biomarkers of peri-implant health or disease

OBJECTIVE

The objective of this study was to investigate the effectiveness of testing for active matrix metalloproteinase-8 (aMMP-8) by a quantitative point-of-care (PoC), chairside lateral flow immunotest and azurocidin, in the peri-implant sulcular fluid (PISF), as biomarkers for the presence or absence of peri-implant diseases.

BACKGROUND

Current research indicates that proinflammatory cytokines and extracellular matrix-degrading enzymes may be of value to diagnose and predict peri-implant disease initiation and progression, but more data are needed.

METHODS

Eighty patients with implants were recruited. PISF samples were collected and quantitatively analyzed for aMMP-8 (chairside) and azurocidin with ELISA. Radiographic assessments and clinical indices (probing depth, probing attachment level, bleeding on probing, and plaque) were recorded after sampling. Kruskal-Wallis test and pairwise post hoc Dunn-Bonferroni test were used to relate aMMP-8 levels and azurocidin levels to clinical parameters. The diagnostic ability of aMMP-8 (ng/mL) and azurocidin was analyzed by receiver operator curve analysis. Area under the curve (AUC) was calculated and the Spearman's rho, and the coefficient of determination (R2) were used to calculate the correlations between aMMP-8, azurocidin, and periodontal parameters.

RESULTS

Statistically significant differences were observed for aMMP-8 levels but not for azurocidin between healthy implants, implants with mucositis, and those with peri-implantitis (13.65 ± 7.18, 32.33 ± 21.20, and 73.07 ± 43.93 ng/mL, respectively), (Kruskall-Wallis test p < .05). The aMMP-8 test with a threshold of 20 ng/mL has a sensitivity of 71.7% and a specificity of 77.8% to identify peri-implantitis and healthy implants, respectively. AUC was found to be 0.814, and the accuracy of the method reaches 73.8%. Above a cutoff value of 33.7 ng/mL of aMMP-8, the accuracy of the test to detect peri-implantitis reaches 77.5% in relation to 62.5% of BoP from the same site.

CONCLUSION

Taken collectively, present data indicate that the aMMP-8 PoC lateral flow immunotest can be a beneficial, adjunctive diagnostic quantitative tool for real-time screening for peri-implant diseases.

Salivary levels of azurocidin and soluble azurophilic granules in periodontal disease

Periodontitis is an infection-driven inflammatory condition of the periodontium. Neutrophils are one of the most important first-line immune cells that protect against pathogen microorganisms in the saliva, but they may also mediate tissue death in inflammatory disorders. The aim of our study was to estimate salivary levels of azurocidin and extracellular azurophilic granules cluster of differentiation (CD63) as biomarkers of neutrophil activation in patients with periodontal diseases and to study the correlation between the levels of these two biomarkers and clinical periodontal parameters. The study included 60 patients with periodontal disease (30 patients with periodontitis and 30 with gingivitis) and 25 healthy controls. The assessed parameters were bleeding on probing, the plaque index, clinical attachment loss, and probing pocket depth. Saliva samples were taken from each study participant, and azurocidin and CD63 levels were measured using ELISA. Azurocidin and CD63 levels were significantly higher in patients with periodontitis and patients with gingivitis than in controls (P < 0.05), and significantly higher in patients with periodontitis than in patients with gingivitis (P < 0.05). Moreover, we found a significant positive correlation between the two biomarkers with clinical attachment loss in the periodontitis group. This study has shown that increased salivary azurocidin and extracellular CD63 levels are associated with enhanced innate response in periodontal disease and can be considered biomarkers of neutrophil activation.

Potential Impact of Prosthetic Biomaterials on the Periodontium: A Comprehensive Review

The success of a prosthetic treatment is closely related to the periodontal health of the individual. The aim of this article was to review and present the importance of prosthetic restorative materials on the condition of the periodontium, the changes that occur in the composition of the subgingival microbiota and the levels of inflammatory markers in gingival crevicular fluid. Articles on the influence of different prosthetic restorative materials on subgingival microbiota and proinflammatory cytokines were searched for using the keywords "prosthetic biomaterials", "fixed prosthesis", "periodontal health", "subgingival microbiota", "periodontal biomarkers" and "gingival crevicular fluid" in PubMed/Medline, Science Direct, Scopus and Google Scholar. The type of material used for prosthesis fabrication together with poor marginal and internal fit can result in changes in the composition of the subgingival microbiota, as well as increased accumulation and retention of dentobacterial plaque, thus favoring the development of periodontal disease and prosthetic treatment failure. Biological markers have helped to understand the inflammatory response of different prosthetic materials on periodontal tissues with the main purpose of improving their clinical application in patients who need them. Metal-free ceramic prostheses induce a lower inflammatory response regardless of the fabrication method; however, the use of CAD/CAM systems is recommended for their fabrication. In addition, it is presumed that metal-ceramic prostheses cause changes in the composition of the subgingival microbiota producing a more dysbiotic biofilm with a higher prevalence of periodontopathogenic bacteria, which may further favor periodontal deterioration.

Next-Generation Examination, Diagnosis, and Personalized Medicine in Periodontal Disease

Periodontal disease, a major cause of tooth loss, is an infectious disease caused by bacteria with the additional aspect of being a noncommunicable disease closely related to lifestyle. Tissue destruction based on chronic inflammation is influenced by host and environmental factors. The treatment of periodontal disease varies according to the condition of each individual patient. Although guidelines provide standardized treatment, optimization is difficult because of the wide range of treatment options and variations in the ideas and skills of the treating practitioner. The new medical concepts of “precision medicine” and “personalized medicine” can provide more predictive treatment than conventional methods by stratifying patients in detail and prescribing treatment methods accordingly. This requires a new diagnostic system that integrates information on individual patient backgrounds (biomarkers, genetics, environment, and lifestyle) with conventional medical examination information. Currently, various biomarkers and other new examination indices are being investigated, and studies on periodontal disease-related genes and the complexity of oral bacteria are underway. This review discusses the possibilities and future challenges of precision periodontics and describes the new generation of laboratory methods and advanced periodontal disease treatment approaches as the basis for this new field.

Metagenomic and Metatranscriptomic Insight Into Oral Biofilms in Periodontitis and Related Systemic Diseases

The oral microbiome is one of the most complex microbial communities in the human body and is closely related to oral and systemic health. Dental plaque biofilms are the primary etiologic factor of periodontitis, which is a common chronic oral infectious disease. The interdependencies that exist among the resident microbiota constituents in dental biofilms and the interaction between pathogenic microorganisms and the host lead to the occurrence and progression of periodontitis. Therefore, accurately and comprehensively detecting periodontal organisms and dissecting their corresponding functional activity characteristics are crucial for revealing periodontitis pathogenesis. With the development of metagenomics and metatranscriptomics, the composition and structure of microbial communities as well as the overall functional characteristics of the flora can be fully profiled and revealed. In this review, we will critically examine the currently available metagenomic and metatranscriptomic evidence to bridge the gap between microbial dysbiosis and periodontitis and related systemic diseases.

The potential impact of salivary peptides in periodontitis

Periodontitis is a complex immune-inflammatory condition characterized by the disruption of the periodontal ligament and subsequent formation of periodontal pockets, and by alveolar bone loss, often resulting in tooth loss. A myriad of factors, namely, genetic, metabolic, immunological, and inflammatory, is associated with progression of periodontitis. Periodontitis is also associated with systemic conditions such as neoplastic disorders, obesity, and diabetes. The current diagnosis of this disease relies on clinical measurements such as clinical attachment loss and probing depth, which have poor precision due to patient, operator and probe-related factors. Thus, there is a need to develop reliable, objective, and reproducible biomarkers for early diagnosis of periodontitis. In this regard, saliva, with contributions from the gingival crevicular fluid, holds great potential. However, most of the information on biomarkers of periodontium-related salivary proteins has come from studies on the molecular pathogenesis of periodontitis. In periodontitis, a more holistic approach, such as the use of -omics technologies, for biomarker discovery, is needed. Herein, we review the biomarkers proposed to date for the assessment of periodontitis, with emphasis on the role of salivary peptides in periodontitis and their assessment by high-throughput saliva proteomics. We also discuss the challenges pertaining to the identification of new periodontitis biomarkers in saliva.

Peptidomic changes of saliva after nonsurgical treatment of stage I / II generalized periodontitis

OBJECTIVE

To explore the changes of peptidome profiles of saliva, serum and gingival crevicular fluid (GCF) before and after nonsurgical periodontal treatment in patients with generalized periodontitis (stage I / II).

SUBJECTS AND METHODS

Saliva, serum and GCF samples were collected from 17 patients at baseline (T₀ ), one week after ultrasonic supragingival scaling (T₁ ) and eight weeks after subgingival scaling and root planning (T₂ ). Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) was carried out to detect changes in peptidomic profiles. Then nano-liquid chromatography-electrospray ionization-tandem mass spectrometry (nano-LC/ESI-MS/MS) was performed to identify potential peptide biomarkers.

RESULTS

Most of the peptides from the patients exhibited a decreasing trend from the time point of pre-treatment to that of post-treatment. Cluster analysis and scatter plots using these peptides indicated that salivary peptidome has an acceptable capability of reflecting the status of stage I / II generalized periodontitis. Seven of these peptides were successfully identified as α-1-antitrypsin, immunoglobulin κ variable 4-1, haptoglobin and immunoglobulin heavy constant γ2.

CONCLUSIONS

Certain peptides in saliva, serum and GCF were down-regulated after nonsurgical periodontal treatment, demonstrating the application prospects of saliva in monitoring and surveillance of periodontal diseases in both clinical settings and communities.

Metaproteome and metabolome of oral microbial communities

The emergence of high-throughput technologies for the comprehensive measurement of biomolecules, also referred to as "omics" technologies, has helped us gather "big data" and characterize microbial communities. In this article, we focus on metaproteomic and metabolomic approaches that support hypothesis-driven investigations on various oral biologic samples. Proteomics reveals the working units of the oral milieu and metabolomics unveils the reactions taking place; and so these complementary techniques can unravel the functionality and underlying regulatory processes within various oral microbial communities. Current knowledge of the proteomic interplay and metabolic interactions of microorganisms within oral biofilm and salivary microbiome communities is presented and discussed, from both clinical and basic research perspectives. Communities indicative of, or from, health, caries, periodontal diseases, and endodontic lesions are represented. Challenges, future prospects, and examples of best practice are given.

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

Gingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal Diseases

Given its intrinsic nature, gingival crevicular fluid (GCF) is an attractive source for the discovery of novel biomarkers of periodontal diseases. GCF contains antimicrobial peptides and small proteins which could play a role in specific immune-inflammatory responses to guarantee healthy gingival status and to prevent periodontal diseases. Presently, several proteomics studies have been performed leading to increased coverage of the GCF proteome, however fewer efforts have been done to explore its natural peptides. To fill such gap, this review provides an overview of the mass spectrometric platforms and experimental designs aimed at GCF peptidome profiling, including our own data and experiences gathered from over several years of matrix-assisted laser desorption ionization/time of flight mass spectrometry (MALDI-TOF MS) based approach in this field. These tools might be useful for capturing snapshots containing diagnostic clinical information on an individual and population scale, which may be used as a specific code not only for the diagnosis of the nature or the stage of the inflammatory process in periodontal disease, but more importantly, for its prognosis, which is still an unmet medical need. As a matter of fact, current peptidomics investigations suffer from a lack of standardized procedures, posing a serious problem for data interpretation. Descriptions of the efforts to address such concerns will be highlighted.

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.

Azurocidin in gingival crevicular fluid as a potential biomarker of chronic periodontitis

BACKGROUND AND OBJECTIVE

Azurocidin is a neutrophil-derived protein in gingival crevicular fluid (GCF) which, according to relevant studies, might correlate with periodontal disease. The aim of the present study was to evaluate azurocidin as a potential biomarker for chronic periodontitis.

MATERIAL AND METHODS

One hundred and one patients participated in the study, divided into two groups. Forty-eight were included in the periodontally healthy group (HP) and fifty-three in the chronic periodontitis group (CP). Clinical indices included probing depth (PD), recession (REC), clinical attachment level (CAL), bleeding on probing (BOP) and plaque (PL). Pooled GCF samples were collected with paper strips, freezed in liquid nitrogen (-196°C), stored at -80°C, and the levels of azurocidin were analyzed with ELISA. Values were transformed and expressed for comparisons in pg/30 s sample. Statistical comparisons were performed using non-parametric tests (Mann-Whitney) at the 0.05 level. Furthermore, the diagnostic accuracy of the procedure was assessed with receiver operator characteristic curves (ROC), areas under the curve (AUC), and the Youden's J Index calculated.

RESULTS

Demographic data were comparable between the two groups. Clinical parameters and the levels of azurocidin were statistically significantly higher in the CP group when compared to the HP group (Mann-Whitney test, P < .05). Quantitative data from ELISA demonstrated a high diagnostic accuracy of azurocidin, with AUC calculated higher than 0.9 at the 0.000 level.

CONCLUSION

Azurocidin in GCF is a promising biomarker for periodontal disease. The results of the present study agree with previous studies in the literature showing an up-regulated trend in the levels of azurocidin in periodontitis patients.

Periodontal disease: From the lenses of light microscopy to the specs of proteomics and next-generation sequencing

Periodontal disease entails the inflammatory destruction of the tooth supporting (periodontal) tissues as a result of polymicrobial colonization of the tooth surface in the form of biofilms. Extensive data collected over the past decades on this chronic disease demonstrate that its progression is infrequent and episodic, and the susceptibility to it can vary among individuals. Physical assessments of previously occurring damage to periodontal tissues remain the cornerstone of detection and diagnosis, whereas traditionally used diagnostic procedures do neither identify susceptible individuals nor distinguish between disease-active and disease-inactive periodontal sites. Thus, more sensitive and accurate "measurable biological indicators" of periodontal diseases are needed in order to place diagnosis (e.g., the presence or stage) and management of the disease on a more rational less empirical basis. Contemporary "omics" technologies may help unlock the path to this quest. High throughput nucleic acid sequencing technologies have enabled us to examine the taxonomic distribution of microbial communities in oral health and disease, whereas proteomic technologies allowed us to decipher the molecular state of the host in disease, as well as the interactive cross-talk of the host with the microbiome. The newly established field of metaproteomics has enabled the identification of the repertoire of proteins that oral microorganisms use to compete or co-operate with each other. Vast such data is derived from oral biological fluids, including gingival crevicular fluid and saliva, which is progressively completed and catalogued as the analytical technologies and bioinformatics tools progressively advance. This chapter covers the current "omics"-derived knowledge on the microbiome, the host and their "interactome" with regard to periodontal diseases, and addresses challenges and opportunities ahead.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.