Novel protein identification methods for biomarker discovery via a proteomic analysis of periodontally healthy and diseased gingival crevicular fluid samples

Gingival Crevicular Fluid as a Novel Potential Source of Biomarkers Distinguishes Pubertal from Post-Pubertal Subjects

Detection of pubertal growth peak is vital in orthodontic treatment timing and planning. Gingival crevicular fluid (GCF) contains abundant proteins from different sources and has been proven to be an ideal source of biomarkers. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS) is an advanced technique that can detect low-molecular-weight peptides with high sensitivity and resolution. The aim of this research was to identify novel candidate biomarkers in GCF to help the diagnosis of pubertal growth peak by MALDI-TOF/MS. Results showed that the peak intensities of six peptides were significantly different between two groups: 1660.2 Da, 1783.0 Da, 2912.5 Da, 4178.6 Da, 5064.9 Da, and 6108.9 Da and are considered to be potential candidate biomarkers to identify pubertal growth peak. Further studies are needed to identify sequence information of these candidate biomarkers.

Metabolomic Analysis of Gingival Crevicular Fluid Using Gas Chromatography/Mass Spectrometry

Periodontitis is one of the most prevalent threats to oral health as the most common cause of tooth loss. In order to perform effective treatment, a clinical test that detect sites where disease activity is high and predicts periodontal tissue destruction is strongly desired, however, it is still difficult to prognose the periodontal tissue breakdown on the basis of conventional methods. The aim of this study is to examine the usefulness of gas chromatography/mass spectrometry (GC/MS), which could eventually be used for on-site analysis of metabolites in gingival crevicular fluid (GCF) in order to objectively diagnose periodontitis at a molecular level. GCF samples were collected from two diseased sites (one site with a moderate pocket and another site with a deep pocket) from each patient and from clinically healthy sites of volunteers. Nineteen metabolites were identified using GC/MS. Total ion current chromatograms showed broad differences in metabolite peak patterns between GCF samples obtained from healthy sites, moderate-pocket sites, and deep-pocket sites. The intensity difference of some metabolites was significant at sites with deep pockets compared to healthy sites. Additionally, metabolite intensities at moderate-pocket sites showed an intermediate profile between the severely diseased sites and healthy sites, which suggested that periodontitis progression could be observed with a changing metabolite profile. Principal component analysis confirmed these observations by clearly delineating healthy sites and sites with deep pockets. These results suggest that metabolomic analysis of GCF could be useful for prediction and diagnosis of periodontal disease in a single visit from a patient and provides the groundwork for establishing a new, on-site diagnostic method for periodontitis.

Advances of Proteomic Sciences in Dentistry

Applications of proteomics tools revolutionized various biomedical disciplines such as genetics, molecular biology, medicine, and dentistry. The aim of this review is to highlight the major milestones in proteomics in dentistry during the last fifteen years. Human oral cavity contains hard and soft tissues and various biofluids including saliva and crevicular fluid. Proteomics has brought revolution in dentistry by helping in the early diagnosis of various diseases identified by the detection of numerous biomarkers present in the oral fluids. This paper covers the role of proteomics tools for the analysis of oral tissues. In addition, dental materials proteomics and their future directions are discussed.

Salivary Cytoprotective Proteins in Inflammation and Resolution during Experimental Gingivitis--A Pilot Study

Objective: The protective mechanisms that maintain periodontal homeostasis in gingivitis and prevent periodontal tissue destruction are poorly understood. The aim of this study was to identify changes in the salivary proteome during experimental gingivitis.

Study design: We used oral neutrophil quantification and whole saliva (WS) proteomics to assess changes that occur in the inflammatory and resolution phases of gingivitis in healthy individuals. Oral neutrophils and WS samples were collected and clinical parameters measured on days 0, 7, 14, 21, 28, and 35.

Results: Increased oral neutrophil recruitment and salivary cytoprotective proteins increased progressively during inflammation and decreased in resolution. Oral neutrophil numbers in gingival inflammation and resolution correlated moderately with salivary β-globin, thioredoxin, and albumin and strongly with collagen alpha-1 and G-protein coupled receptor 98.

Conclusions: Our results indicate that changes in salivary cytoprotective proteins in gingivitis are associated with a similar trend in oral neutrophil recruitment and clinical parameters.

Clinical relevance: We found moderate to strong correlations between oral neutrophil numbers and levels of several salivary cytoprotective proteins both in the development of the inflammation and in the resolution of gingivitis. Our proteomics approach identified and relatively quantified specific cytoprotective proteins in this pilot study of experimental gingivitis; however, future and more comprehensive studies are needed to clearly identify and validate those protein biomarkers when gingivitis is active.

Gingival crevicular fluid proteomes in health, gingivitis and chronic periodontitis

OBJECTIVE

The aim of this study was to compare the proteome composition of gingival crevicular fluid obtained from healthy periodontium, gingivitis and chronic periodontitis affected sites.

BACKGROUND

Owing to its site-specific nature, gingival crevicular fluid is ideal for studying biological processes that occur during periodontal health and disease progression. However, few studies have been conducted into the gingival crevicular fluid proteome due to the small volumes obtained.

METHODS

Fifteen males were chosen for each of three different groups, healthy periodontium, gingivitis and chronic periodontitis. They were categorized based on clinical measurements including probing depth, bleeding on probing, plaque index, radiographic bone level, modified gingival index and smoking status. Gingival crevicular fluid was collected from each patient, pooled into healthy, gingivitis and chronic periodontitis groups and their proteome analyzed by gel electrophoresis and liquid chromatography electrospray ionization ion trap tandem mass spectrometry.

RESULTS

One hundred and twenty-one proteins in total were identified, and two-thirds of these were identified in all three conditions. Forty-two proteins were considered to have changed in abundance. Of note, cystatin B and cystatin S decreased in abundance from health to gingivitis and further in chronic periodontitis. Complement proteins demonstrated an increase from health to gingivitis followed by a decrease in chronic periodontitis. Immunoglobulins, keratin proteins, fibronectin, lactotransferrin precursor, 14-3-3 protein zeta/delta, neutrophil defensin 3 and alpha-actinin exhibited fluctuations in levels.

CONCLUSION

The gingival crevicular fluid proteome in each clinical condition was different and its analysis may assist us in understanding periodontal pathogenesis.

Physiological adaptations of key oral bacteria

Oral colonising bacteria are highly adapted to the various environmental niches harboured within the mouth, whether that means while contributing to one of the major oral diseases of caries, pulp infections, or gingival/periodontal disease or as part of a commensal lifestyle. Key to these infections is the ability to adhere to surfaces via a range of specialised adhesins targeted at both salivary and epithelial proteins, their glycans and to form biofilm. They must also resist the various physical stressors they are subjected to, including pH and oxidative stress. Possibly most strikingly, they have developed the ability to harvest both nutrient sources provided by the diet and those derived from the host, such as protein and surface glycans. We have attempted to review recent developments that have revealed much about the molecular mechanisms at work in shaping the physiology of oral bacteria and how we might use this information to design and implement new treatment strategies.

Uncovering the molecular networks in periodontitis

Periodontitis is a complex immune-inflammatory disease that results from a preestablished infection in gingiva, mainly due to Gram-negative bacteria that colonize deeper in gingival sulcus and latter periodontal pocket. Host inflammatory and immune responses have both protective and destructive roles. Although cytokines, prostaglandins, and proteases struggle against microbial burden, these molecules promote connective tissue loss and alveolar bone resorption, leading to several histopathological changes, namely destruction of periodontal ligament, deepening of periodontal pocket, and bone loss, which can converge to attain tooth loss. Despite the efforts of genomics, transcriptomics, proteomics/peptidomics, and metabolomics, there is no available biomarker for periodontitis diagnosis, prognosis, and treatment evaluation, which could assist on the established clinical evaluation. Nevertheless, some genes, transcripts, proteins and metabolites have already shown a different expression in healthy subjects and in patients. Though, so far, 'omics approaches only disclosed the host inflammatory response as a consequence of microbial invasion in periodontitis and the diagnosis in periodontitis still relies on clinical parameters, thus a molecular tool for assessing periodontitis lacks in current dental medicine paradigm. Saliva and gingival crevicular fluid have been attracting researchers due to their diagnostic potential, ease, and noninvasive nature of collection. Each one of these fluids has some advantages and disadvantages that are discussed in this review.

Quantitative gingival crevicular fluid proteome in health and periodontal disease using stable isotope chemistries and mass spectrometry

AIM

Application of quantitative stable isotope-labelling chemistries and mass spectrometry (MS) to determine alterations in gingival crevicular fluid (GCF) proteome in periodontal disease.

MATERIAL AND METHODS

Quantitative proteome of GCF from 40 healthy individuals versus 40 patients with periodontal disease was established using 320 GCF samples and stable isotope-labelling reagents, ICAT and mTRAQ, with MS technology and validated by enzyme-linked immunosorbent methods.

RESULTS

We have identified 238 distinct proteins of which 180 were quantified in GCF of both healthy and periodontal patients with additional 26 and 32 distinct proteins that were found only in GCF of healthy or periodontal patients. In addition, 42 pathogenic bacterial proteins and 11 yeast proteins were quantified. The data highlighted a series of proteins not quantified previously by large-scale MS approaches in GCF with relevance to periodontal disease, such as host-derived Ig alpha-2 chain C, Kallikrein-4, S100-A9, transmembrane proteinase 13, peptidase S1 domain, several collagen types and pathogenic bacterial proteins, e.g. formamidase, leucine aminopeptidase and virulence factor OMP85.

CONCLUSIONS

The innovative analytical approaches provided detailed novel changes in both host and microbial derived GCF proteomes of periodontal patients. The study defined 50 host and 16 pathogenic bacterial proteins significantly elevated in periodontal disease most of which were novel with significant potential for application in the clinical arena of periodontal disease.

Crevicular fluid biomarkers and periodontal disease progression

AIM

Assess the ability of a panel of gingival crevicular fluid (GCF) biomarkers as predictors of periodontal disease progression (PDP).

MATERIALS AND METHODS

In this study, 100 individuals participated in a 12-month longitudinal investigation and were categorized into four groups according to their periodontal status. GCF, clinical parameters and saliva were collected bi-monthly. Subgingival plaque and serum were collected bi-annually. For 6 months, no periodontal treatment was provided. At 6 months, patients received periodontal therapy and continued participation from 6 to 12 months. GCF samples were analysed by ELISA for MMP-8, MMP-9, Osteoprotegerin, C-reactive Protein and IL-1β. Differences in median levels of GCF biomarkers were compared between stable and progressing participants using Wilcoxon Rank Sum test (p = 0.05). Clustering algorithm was used to evaluate the ability of oral biomarkers to classify patients as either stable or progressing.

RESULTS

Eighty-three individuals completed the 6-month monitoring phase. With the exception of GCF C-reactive protein, all biomarkers were significantly higher in the PDP group compared to stable patients. Clustering analysis showed highest sensitivity levels when biofilm pathogens and GCF biomarkers were combined with clinical measures, 74% (95% CI = 61, 86).

CONCLUSIONS

Signature of GCF fluid-derived biomarkers combined with pathogens and clinical measures provides a sensitive measure for discrimination of PDP (ClinicalTrials.gov NCT00277745).

Proteomics for the discovery of biomarkers and diagnosis of periodontitis: a critical review

Periodontitis is a common chronic and destructive disease whose pathogenetic mechanisms remain unclear. Due to their sensitivity and global scale, proteomics studies offer the opportunity to uncover critical host and pathogen activity indicators and can elucidate clinically applicable biomarkers for improved diagnosis and treatment of the disease. This review summarizes the literature of proteomics studies on periodontitis and comprehensively discusses commonly found candidate biomarkers. Key considerations in the design of an experimental proteomics platform are also outlined. The applicability of protein biomarkers across the progression of periodontitis and unexplored areas of research are highlighted.

Quantitative proteomic analysis of gingival crevicular fluid in different periodontal conditions

AIM

To quantify the proteome composition of the GCF in periodontal health (HH) and in sites with different clinical conditions in chronic periodontitis (CP) subjects.

MATERIAL AND METHODS

5 subjects with HH and 5 with CP were submitted to full-mouth periodontal examination, and GCF sampling. Sites in the CP group were classified and sampled as periodontitis (P, probing depth, PD>4 mm), gingivitis (G, PD≤3 mm with bleeding on probing, BOP), and healthy sites (H, PD≤3 mm without BOP). GCF proteins were subjected to liquid chromatography electrospray ionization mass spectrometry for identification, characterization and quantification.

RESULTS

230 proteins were identified; 145 proteins were detected in HH, 214 in P, 154 in G, and 133 in H. Four proteins were exclusively detected at HH, 43 proteins at P, 7 proteins at G, and 1 protein at H. Compared to HH group, 35 and 6 proteins were more abundant in P and G (p<0.001), respectively; and 4, 15 and 37 proteins were less abundant in P, G and H (p≤0.01), respectively.

CONCLUSIONS

There are marked differences in the GCF proteome according to disease profile. Comprehension of the role of the identified proteins in the etiopathogenesis of periodontal disease may lead to biomarkers definition.

Application of quantitative proteomic analysis using tandem mass tags for discovery and identification of novel biomarkers in periodontal disease

Periodontal disease is a bacterial infection that destroys the gingiva and surrounding tissues of the oral cavity. Gingival crevicular fluid (GCF) is extracted from the gingival sulcus and pocket. Analysis of biochemical markers in GCF, which predict the progression of periodontal disease, may facilitate disease diagnosis. However, no useful GCF biochemical markers with high sensitivity for detecting periodontal disease have been identified. Thus, the search for biochemical markers of periodontal disease is of continued interest in experimental and clinical periodontal disease research. Using tandem mass tag (TMT) labeling, we analyzed GCF samples from healthy subjects and patients with periodontal disease, and identified a total of 619 GCF proteins based on proteomic analysis. Of these, we focused on two proteins, matrix metalloproteinase (MMP)-9 and neutrophil gelatinase-associated lipocalin (LCN2), which are involved in the progression of periodontal disease. Western blot analysis revealed that the levels of MMP-9 and LCN2 were significantly higher in patients with periodontal disease than in healthy subjects. In addition, ELISA also detected significantly higher levels of LCN2 in patients with periodontal disease than in healthy subjects. Thus, LC-MS/MS analyses of GCF using TMT labeling led to the identification of LCN2, which may be a promising GCF biomarker for the detection of periodontal disease.

Non-bacterial protein expression in periodontal pockets by proteome analysis

OBJECTIVES

To compare the proteomic profile of inter-proximal pocket tissues with inter-proximal healthy tissues in the same subject to reveal proteins associated with periodontal disease in sites where periodontopathogenic bacteria were not detectable.

METHODS

Twenty-five healthy patients, with moderate-to-advanced chronic periodontitis and presenting with at least one intra-bony defect next to a healthy inter-proximal site were enrolled. The periodontal defects were treated with osseous resective surgery, and the flap design included both the periodontal pockets and the neighbouring inter-proximal healthy sites. Pocket-associated and healthy tissues were harvested for proteomic analyses.

RESULTS

Fifteen proteins were differently expressed between pathological and healthy tissues. In particular, annexin A2, actin cytoplasmic 1, carbonic anhydrase 1 & 2; Ig kappa chain C region (two spots) and flavinreductase were overexpressed, whereas 14-3-3 protein sigma and zeta/delta, heat-shock protein beta -1 (two spots), triosephosphateisomerase, peroxiredoxin-1, fatty acid-binding protein-epidermal, and galectin-7 were underexpressed in pathological tissue.

CONCLUSIONS

The unbalanced functional network of proteins involved could hinder adequate tissue response to pathogenic noxa. The study of periodontal pocket tissue proteomic profile would be crucial to better understand the pathogenesis of and the therapeutic strategies for periodontitis.

Salivary concentration of free LL-37 in edentulism, chronic periodontitis and healthy periodontium

OBJECTIVE

The antimicrobial peptide LL-37, a component of innate immunity, has an important role in maintaining oral health. This study aimed to investigate the concentration of free LL-37 in whole saliva of periodontally healthy, edentulous and chronic periodontitis subjects.

DESIGN

Unstimulated whole saliva was sampled from 154 subjects (76 periodontally healthy, 20 edentulous, and 58 subjects with chronic periodontitis). All participants were in good general health. The salivary LL-37 was determined by ELISA.

RESULTS

The median salivary concentrations of free LL-37 were 30.5, 22.5, and 1.8ng/ml for the healthy, the chronic periodontitis and the edentulous group, respectively. The differences in concentration between the edentulous and the others were statistically significant (Mann-Whitney test, p<0.001). In the healthy subjects, women displayed significantly higher peptide concentrations compared to men (Mann-Whitney test, p<0.05). The intra-subject variation in LL-37 concentration was wider for the healthy (range 0.75-285ng/ml) and chronic periodontitis patients (range 1-207ng/ml) than for the edentulous subjects (range 0.15-4.4ng/ml).

CONCLUSIONS

The present findings show that edentulism correlates with a substantial decrease in salivary levels of free LL-37, thus indicating the considerable contribution of the gingival tissues in the secretion of the peptide in the oral environment.

Label-free quantitative proteomics reveals differentially regulated proteins in experimental gingivitis

We investigated the sequential protein expression in gingival crevicular fluid samples during the induction (I) and resolution (R) of experimental gingivitis. Periodontally and systemically healthy volunteers (n = 20) participated in a three-week experimental gingivitis protocol, followed by debridement and two weeks of regular plaque control. Gingival crevicular fluid (GCF) samples were collected at baseline, Day 7, 14, and 21 (induction; I-phase), and at Day 21, 25, 30, and 35 (resolution; R-phase). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) for label-free quantitative proteomics was applied. A total of 287 proteins were identified including 254 human, 14 bacterial, 12 fungal, and 7 yeast proteins. Ontology analysis revealed proteins primarily involved in cytoskeletal rearrangements, immune response, antimicrobial function, protein degradation, and DNA binding. There was considerable variation in the number of proteins identified, both among subjects and within subjects across time points. After pooling of samples between subjects at each time point, the levels of 59 proteins in the I-phase and 73 proteins in the R-phase were quantified longitudinally. Our data demonstrate that LC-MS/MS label-free quantitative proteomics is valuable in the assessment of the protein content of the GCF and can facilitate a better understanding of the molecular mechanisms involved in the induction and resolution of plaque-induced gingival inflammation in humans.

Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization

AIM

To identify optimal combination(s) of proteomic based biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals and validate the predictions through known and blind test sets.

MATERIALS AND METHODS

GCF samples were collected from 96 CP and periodontally healthy subjects and analysed using high-performance liquid chromatography, tandem mass spectrometry and the PILOT_PROTEIN algorithm. A mixed-integer linear optimization (MILP) model was then developed to identify the optimal combination of biomarkers which could clearly distinguish a blind subject sample as healthy or diseased.

RESULTS

A thorough cross-validation of the MILP model capability was performed on a training set of 55 samples and greater than 99% accuracy was consistently achieved when annotating the testing set samples as healthy or diseased. The model was then trained on all 55 samples and tested on two different blind test sets, and using an optimal combination of 7 human proteins and 3 bacterial proteins, the model was able to correctly predict 40 out of 41 healthy and diseased samples.

CONCLUSIONS

The proposed large-scale proteomic analysis and MILP model led to the identification of novel combinations of biomarkers for consistent diagnosis of periodontal status with greater than 95% predictive accuracy.

Host genome, epigenome, and oral microbiome interactions: toward personalized periodontal therapy

Periodontal diseases are multidimensional and complex. Bacterial content is the initiator, but disease progression depends on genetic and environmental parameters related to the host. Although bone loss magnitude is the common resulting outcome, the biologic process likely represents a unique inflammatory response characteristic to every individual. Therefore, it is obvious that practitioners must take into account the influence of these parameters and tailor a treatment accordingly. New, emerging deoxyribonucleotide-based technologies allow integration of the biologic impact of the environment, and periodontists should be prepared to incorporate these technologies into their practice to advance personalized medicine. This commentary provides updated insights on the distinctiveness of inflammation per individual in terms of microbiome and genome specificity and cites some educational resources helpful for implementing individualized therapy.

Mass spectrometric analysis of gingival crevicular fluid biomarkers can predict periodontal disease progression

BACKGROUND AND OBJECTIVE

Gingival crevicular fluid has been suggested as a possible source of biomarkers for periodontal disease progression. This paper describes a technique for the analysis of gingival crevicular fluid from individual sites using mass spectrometry. It explores the novel use of mass spectrometry to examine the relationship between the relative amounts of proteins and peptides in gingival crevicular fluid and their relationship with clinical indices and periodontal attachment loss in periodontal maintenance patients. The aim of this paper was to assess whether the mass spectrometric analysis of gingival crevicular fluid may allow for the site-specific prediction of periodontal disease progression.

MATERIAL AND METHODS

Forty-one periodontal maintenance subjects were followed over 12 mo, with clinical measurements taken at baseline and every 3 mo thereafter. Gingival crevicular fluid was collected from subjects at each visit and was analysed using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Samples were classified based upon pocket depth, modified gingival index (MGI), plaque index and attachment loss, and were analysed within these groups. A genetic algorithm was used to create a model based on pattern analysis to predict sites undergoing attachment loss.

RESULTS

Three hundred and eighty-five gingival crevicular fluid samples were analysed. Twenty-five sites under observation in 14 patients exhibited attachment loss of > 2 mm over the 12-mo period. The clinical indices pocket depth, MGI, plaque levels and bleeding on probing served as poor discriminators of gingival crevicular fluid mass spectra. Models generated from the gingival crevicular fluid mass spectra could predict attachment loss at a site with a high specificity (97% recognition capability and 67% cross-validation).

CONCLUSIONS

Gingival crevicular fluid mass spectra could be used to predict sites with attachment loss. The use of algorithm-generated models based on gingival crevicular fluid mass spectra may provide utility in the diagnosis of periodontal disease.

PILOT_PROTEIN: identification of unmodified and modified proteins via high-resolution mass spectrometry and mixed-integer linear optimization

A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at http://pumpd.princeton.edu.

Salivary proteome modifications associated with periodontitis in obese patients

AIM

To identify changes in the salivary protein/peptide profiles by differential proteomics in obese patients with or without periodontitis.

MATERIAL AND METHODS

Periodontal examinations and whole saliva samples were obtained from 38 obese patients (mean age: 45.1 ± 7.3 years, mean BMI: 49.3 ± 9 kg/m(2) ) including 13 periodontitis and 25 non-periodontitis subjects, and 19 healthy controls (mean age: 44.2 ± 6.4 years, mean BMI: 21.5 ± 2.1 kg/m(2) ). Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) was used to compare the whole saliva polypeptide profiles.

RESULTS

The SELDI-TOF-MS analysis detected eight putative markers. Six of them were increased and identified in obese subjects versus controls (albumin, α and β haemoglobin chains, α-defensins 1, 2 and 3). Alpha-defensins were less abundant in saliva of periodontitis obese patients (36.47 ± 19.84 μA) versus non-periodontitis obese patients (43.44 ± 30.34  μA), whereas α-defensins were more abundant in obese patients (40.99 ± 26.66  μA) versus controls (27.1 ± 23.98  μA).

CONCLUSIONS

Periodontal status modifies the salivary proteome in obese patients. Alpha-defensins may play a role in gingival inflammation, and be involved in the higher susceptibility of obese patients to periodontal diseases.