Probing the salivary proteome for prognostic biomarkers in response to non-surgical periodontal therapy.

AIM: This prospective study investigated the salivary proteome before and after periodontal therapy. MATERIALS AND METHODS: Ten systemically healthy, non-smoking, stage III, grade C periodontitis patients underwent non-surgical periodontal treatment. Full-mouth periodontal parameters were measured, and saliva (n = 30) collected pre- (T0), and one (T1) and six (T6) months post-treatment. The proteome was investigated by label-free quantitative proteomics. Protein expression changes were modelled over time, with significant protein regulation considered at false discovery rate <0.05. RESULTS: Treatment significantly reduced bleeding scores, percentages of sites with pocket depth ≥5 mm, plaque and gingival indexes. One thousand seven hundred and thirteen proteins were identified and 838 proteins (human = 757, bacterial = 81) quantified (≥2 peptides). At T1, 80 (T1 vs. T0: 60↑:20↓), and at T6, 118 human proteins (T6 vs. T0: 67↑:51↓) were regulated. The salivary proteome at T6 versus T1 remained stable. Highest protein activity post- versus pre-treatment was observed for cellular movement and inflammatory response. The small proline-rich protein 3 (T1 vs. T0: 5.4-fold↑) and lymphocyte-specific protein 1 (T6 vs. T0: 4.6-fold↓) were the top regulated human proteins. Proteins from Neisseria mucosa and Treponema socranskii (T1 vs. T0: 8.0-fold↓, 4.9-fold↓) were down-regulated. CONCLUSIONS: Periodontal treatment reduced clinical disease parameters and these changes were reflected in the salivary proteome. This underscores the potential of utilizing saliva biomarkers as prognostic tools for monitoring treatment outcomes.

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.

ZW800-PEG: A Renal Clearable Zwitterionic Near-Infrared Fluorophore for Potential Clinical Translation.

Near-infrared (NIR) fluorescence imaging has advanced medical imaging and image-guided interventions during the past three decades. Despite tremendous advances in imaging devices, surprisingly only a few dyes are currently available in the clinic. Previous fluorophores, ZW800-1A and ZW800-1C, significantly improved the poor performance of the FDA-approved indocyanine green. However, ZW800-1A is not stable in serum and ZW800-1C induces severe stacking in aqueous media. To solve such dilemmas, ZW800-PEG was designed by introducing a flexible yet stable thiol PEG linker. ZW800-PEG shows high solubility in both aqueous and organic solvents, thus improving renal clearance with minimal binding to serum proteins during systemic circulation. The sulfide group on the meso position of the heptamethine core improves serum stability and physicochemical properties including the maximum emission wavelength shift to 800 nm, enabling the use of ZW800-PEG for image-guided interventions and augmenting photothermal therapy.

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.

Salivary proteotypes of gingivitis tolerance and resilience.

AIM: This study aimed to characterize the salivary proteome during the induction and resolution of gingival inflammation in the course of human experimental gingivitis (EG), and to cluster the proteomic profiles based on the clinically defined "slow" and "fast" response patterns. MATERIALS AND METHODS: A total of 50 unstimulated whole saliva were obtained from the EG model which was induced over 21 days (days 0, 7, 14 and 21), followed by a two-week resolution phase (day 35). Label-free quantitative proteomics using liquid chromatography-tandem mass spectrometry was applied. Regulated proteins were subject to Gene Ontology enrichment analysis. RESULTS: A total of 804 human proteins were quantified by ≥ 2 peptides. Principal component analysis depicted significant differences between "fast" and "slow" responders. Despite gingival and plaque scores being similar at baseline among the two groups, "fast" responders presented with 48 proteins that were at > 4-fold higher levels than "slow" responders. These up-regulated proteins showed enrichment in "antigen presentation" and "proteolysis." CONCLUSIONS: Together, these findings highlight the utility of integrative systems-level quantitative proteomic approaches to unravel the molecular basis of "salivary proteotypes" associated with gingivitis dubbed as "fast" and "slow" responders. Hence, these differential responses may help prognosticate individual susceptibility to gingival inflammation.

Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease.

Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. They affect the tooth-supporting tissues with multiple ramifications on life quality. Their early diagnosis is still challenging, due to lack of appropriate molecular diagnostic methods. Saliva offers a non-invasively collectable reservoir of clinically relevant biomarkers, which, if utilized efficiently, could facilitate early diagnosis and monitoring of ongoing disease. Despite several novel protein markers being recently enlisted by discovery proteomics, their routine diagnostic application is hampered by the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. Here we carried out a pipeline of two proteomic platforms; firstly, we applied open ended label-free quantitative (LFQ) proteomics for discovery in saliva (n = 67, including individuals with health, gingivitis, and periodontitis), followed by selected-reaction monitoring (SRM)-targeted proteomics for validation in an independent cohort (n = 82). The LFQ platform led to the discovery of 119 proteins with at least 2-fold significant difference between health and disease. The 65 proteins chosen for the subsequent SRM platform included 50 functionally related proteins derived from the significantly enriched processes of the LFQ data, 11 from literature-mining, and four house-keeping ones. Among those, 60 were reproducibly quantifiable proteins (92% success rate), represented by a total of 143 peptides. Machine-learning modeling led to a narrowed-down panel of five proteins of high predictive value for periodontal diseases with maximum area under the receiver operating curve >0.97 (higher in disease: Matrix metalloproteinase-9, Ras-related protein-1, Actin-related protein 2/3 complex subunit 5; lower in disease: Clusterin, Deleted in Malignant Brain Tumors 1). This panel enriches the pool of credible clinical biomarker candidates for diagnostic assay development. Yet, the quantum leap brought into the field of periodontal diagnostics by this study is the application of the biomarker discovery-through-verification pipeline, which can be used for validation in further cohorts.

Gingival Exudatome Dynamics Implicate Inhibition of the Alternative Complement Pathway in the Protective Action of the C3 Inhibitor Cp40 in Nonhuman Primate Periodontitis.

Periodontitis is a prevalent chronic inflammatory disease associated with dysbiosis. Although complement inhibition has been successfully used to treat periodontitis in animal models, studies globally analyzing inflamed tissue proteins to glean insight into possible mechanisms of action are missing. Using quantitative shotgun proteomics, we aimed to investigate differences in composition of inflammatory gingival tissue exudate ("gingival crevicular fluid"; GCF), before and after local administration of an inhibitor of the central complement component, C3, in nonhuman primates. The C3 inhibitor, Cp40 (also known as AMY-101) was administered locally in the maxillary gingival tissue of cynomolgus monkeys with established periodontitis, either once a week (1×-treatment; n = 5 animals) or three times per week (3×-treatment; n = 10 animals), for 6 weeks followed by another 6 weeks of observation in the absence of treatment. 45 GCF samples were processed for FASP digestion and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Data were processed using the ProgenesisQI software. The statistical significance of differences between the groups was determined by RM-ANOVA, and a protein expression change was considered as a true regulation at >2-fold and p < 0.05. The human orthologues were subjected to Gene Ontology analyses using PANTHER. Data are available via ProteomeXchange with identifier PXD009502. 573 proteins with >2 peptides were longitudinally quantified. Both 3× and 1× administration of Cp40 resulted in significant down-regulation of dozens of proteins during the 6-week course of treatment as compared to baseline. Following drug withdrawal at 6 weeks, more than 50% of the down-regulated proteins showed increased levels at week 12. The top scored pathway was "complement activation, alternative pathway", and several proteins involved in this pathway were down-regulated at 6 weeks. We mapped the proteomic fingerprint changes in local tissue exudate of cynomolgus monkey periodontitis in response to C3 inhibition and identified the alternative pathway of complement activation and leukocyte degranulation as main targets, which are thus likely to play significant roles in periodontal disease pathogenesis. Label-free quantitative proteomics strategies utilizing GCF are powerful tools for the identification of treatment targets and providing insights into disease mechanisms.

Contribution of proteomics to our understanding of periodontal inflammation.

Periodontal diseases entail the inflammatory destruction of the tooth supporting (periodontal) tissues and may eventually lead tooth loss. Severe periodontal disease (or periodontitis) affects approximately 10% of the global population. Periodontitis not only severely deteriorates people's quality of life by impairing the dentition but also adversely affects systemic health. The present review paper highlights the advancements made in our understanding of inflammatory periodontal diseases by the use of proteomic technologies. The novel information comes from both clinical and in vitro studies, the former investigating samples of saliva and gingival crevicular fluid in patients with periodontal disease, whereas the latter utilizing host cell/tissue-bacteria/biofilm interaction models of relevance to periodontal disease. A broad range of information on protein profiles can be obtained, which is useful, however needs to be individually validated by golden-standard sensitive antibody-based methods. The development of the employed proteomic platform technologies will help complete in breadth and in depth the protein profiles of periodontal disease. The so-far collected data highlights the importance of moving away from the concept of a handful of proteins being responsible for the pathogenesis of the disease, and start to accept that there are "signatures" of proteins and associated pathways that lead to this.

Role of Porphyromonas gingivalis gingipains in multi-species biofilm formation.

BACKGROUND: Periodontal diseases are polymicrobial diseases that cause the inflammatory destruction of the tooth-supporting (periodontal) tissues. Their initiation is attributed to the formation of subgingival biofilms that stimulate a cascade of chronic inflammatory reactions by the affected tissue. The Gram-negative anaerobes Porphyromonas gingivalis, Tannerella forsythia and Treponema denticola are commonly found as part of the microbiota of subgingival biofilms, and they are associated with the occurrence and severity of the disease. P. gingivalis expresses several virulence factors that may support its survival, regulate its communication with other species in the biofilm, or modulate the inflammatory response of the colonized host tissue. The most prominent of these virulence factors are the gingipains, which are a set of cysteine proteinases (either Arg-specific or Lys-specific). The role of gingipains in the biofilm-forming capacity of P. gingivalis is barely investigated. Hence, this in vitro study employed a biofilm model consisting of 10 "subgingival" bacterial species, incorporating either a wild-type P. gingivalis strain or its derivative Lys-gingipain and Arg-gingipan isogenic mutants, in order to evaluate quantitative and qualitative changes in biofilm composition. RESULTS: Following 64 h of biofilm growth, the levels of all 10 species were quantified by fluorescence in situ hybridization or immunofluorescence. The wild-type and the two gingipain-deficient P. gingivalis strains exhibited similar growth in their corresponding biofilms. Among the remaining nine species, only the numbers of T. forsythia were significantly reduced, and only when the Lys-gingipain mutant was present in the biofilm. When evaluating the structure of the biofilm by confocal laser scanning microscopy, the most prominent observation was a shift in the spatial arrangement of T. denticola, in the presence of P. gingivalis Arg-gingipain mutant. CONCLUSIONS: The gingipains of P. gingivalis may qualitatively and quantitatively affect composition of polymicrobial biofilms. The present experimental model reveals interdependency between the gingipains of P. gingivalis and T. forsythia or T. denticola.

Establishment and characterization of immortalized gingival epithelial and fibroblastic cell lines for the development of organotypic cultures.

In vitro studies using 3D co-cultures of gingival cells can resemble their in vivo counterparts much better than 2D models that typically only utilize monolayer cultures with short-living primary cells. However, the use of 3D gingival models is still limited through lack of appropriate cell lines. We aimed to establish immortalized cell line models of primary human gingival epithelium keratinocytes (HGEK) and gingival fibroblasts (GFB). Immortalized cell lines (HGEK-16 and GFB-16) were induced by E6 and E7 oncoproteins of human papillomavirus. In addition, 3D multilayered organotypic cultures were formed by embedding GFB-16 cells within a collagen (Col) matrix and seeding of HGEK-16 cells on the upper surfaces. Cell growth was analyzed in both immortalized cell lines and their parental primary cells. The expression levels of cell type-specific markers, i.e. cytokeratin (CK) 10, CK13, CK16, CK18, CK19 for HGEK-16 and Col I and Col II for GFB-16, were evaluated by quantitative real-time polymerase chain reaction (qRT-PCR). Expansion of the primary cultures was impeded at early passages, while the transformed immortalized cell lines could be expanded for more than 30 passages. In 3D cultures, immortalized HGEK formed a multilayer of epithelial cells. qRT-PCR showed that cell-specific marker expression in the 3D cultures was qualitatively and quantitatively closer to that in human gingival tissue than to monolayer cultures. These results indicate that immortalized gingival fibroblastic and epithelial cell lines can successfully form organotypic multilayered cultures and, therefore, may be useful tools for studying gingival tissue in vitro.

Proteome Analysis of Oral Biofluids in Periodontal Health and Disease Using Mass Spectrometry.

Mass spectrometry-based proteomic approaches permit the high-throughput assessment of proteins from oral biofluids, therefore, allowing a deeper insight into the mechanistic study of periodontal disease. Here we describe an entire experimental design of proteomic workflow for oral biofluids, exemplified by saliva and gingival crevicular fluid collected from periodontal health or disease subjects and using a label-free quantification strategy for mass spectrometric data acquisition.

Proteomic Characterization of the Oral Pathogen Filifactor alocis Reveals Key Inter-Protein Interactions of Its RTX Toxin: FtxA.

Filifactor alocis is a Gram-positive asaccharolytic, obligate anaerobic rod that has been isolated from a variety of oral infections including periodontitis, peri-implantitis, and odontogenic abscesses. As a newly emerging pathogen, its type strain has been investigated for pathogenic properties, yet little is known about its virulence variations among strains. We previously screened the whole genome of nine clinical oral isolates and a reference strain of F. alocis, and they expressed a novel RTX toxin, FtxA. In the present study, we aimed to use label-free quantification proteomics to characterize the full proteome of those ten F. alocis strains. A total of 872 proteins were quantified, and 97 among them were differentially expressed in FtxA-positive strains compared with the negative strains. In addition, 44 of these differentially expressed proteins formed 66 pairs of associations based on their predicted functions, which included clusters of proteins with DNA repair/mediated transformation and catalytic activity-related function, indicating different biosynthetic activities among strains. FtxA displayed specific interactions with another six intracellular proteins, forming a functional cluster that could discriminate between FtxA-producing and non-producing strains. Among them were FtxB and FtxD, predicted to be encoded by the same operon as FtxA. While revealing the broader qualitative and quantitative proteomic landscape of F. alocis, this study also sheds light on the deeper functional inter-relationships of FtxA, thus placing this RTX family member into context as a major virulence factor of this species.

Validation and verification of predictive salivary biomarkers for oral health.

Oral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.

Salivary Biomarkers for Dental Caries Detection and Personalized Monitoring.

This study investigated the potential of salivary bacterial and protein markers for evaluating the disease status in healthy individuals or patients with gingivitis or caries. Saliva samples from caries- and gingivitis-free individuals (n = 18), patients with gingivitis (n = 17), or patients with deep caries lesions (n = 38) were collected and analyzed for 44 candidate biomarkers (cytokines, chemokines, growth factors, matrix metalloproteinases, a metallopeptidase inhibitor, proteolytic enzymes, and selected oral bacteria). The resulting data were subjected to principal component analysis and used as a training set for random forest (RF) modeling. This computational analysis revealed four biomarkers (IL-4, IL-13, IL-2-RA, and eotaxin/CCL11) to be of high importance for the correct depiction of caries in 37 of 38 patients. The RF model was then used to classify 10 subjects (five caries-/gingivitis-free and five with caries), who were followed over a period of six months. The results were compared to the clinical assessments of dental specialists, revealing a high correlation between the RF prediction and the clinical classification. Due to the superior sensitivity of the RF model, there was a divergence in the prediction of two caries and four caries-/gingivitis-free subjects. These findings suggest IL-4, IL-13, IL-2-RA, and eotaxin/CCL11 as potential salivary biomarkers for identifying noninvasive caries. Furthermore, we suggest a potential association between JAK/STAT signaling and dental caries onset and progression.

OralDisk: A Chair-Side Compatible Molecular Platform Using Whole Saliva for Monitoring Oral Health at the Dental Practice.

Periodontitis and dental caries are two major bacterially induced, non-communicable diseases that cause the deterioration of oral health, with implications in patients' general health. Early, precise diagnosis and personalized monitoring are essential for the efficient prevention and management of these diseases. Here, we present a disk-shaped microfluidic platform (OralDisk) compatible with chair-side use that enables analysis of non-invasively collected whole saliva samples and molecular-based detection of ten bacteria: seven periodontitis-associated (Aggregatibacter actinomycetemcomitans, Campylobacter rectus, Fusobacterium nucleatum, Prevotella intermedia, Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola) and three caries-associated (oral Lactobacilli, Streptococcus mutans, Streptococcus sobrinus). Each OralDisk test required 400 µL of homogenized whole saliva. The automated workflow included bacterial DNA extraction, purification and hydrolysis probe real-time PCR detection of the target pathogens. All reagents were pre-stored within the disk and sample-to-answer processing took < 3 h using a compact, customized processing device. A technical feasibility study (25 OralDisks) was conducted using samples from healthy, periodontitis and caries patients. The comparison of the OralDisk with a lab-based reference method revealed a ~90% agreement amongst targets detected as positive and negative. This shows the OralDisk's potential and suitability for inclusion in larger prospective implementation studies in dental care settings.

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.

Dysbiosis of the Oral Ecosystem in Severe Congenital Neutropenia Patients.

PURPOSE: To decipher the underlying immunological mechanisms in predisposition to oral microbial dysbiosis in severe congenital neutropenia (SCN) patients. EXPERIMENTAL DESIGN: Ten SCN patients (5-23 years old) and 12 healthy controls (5-22 years old) are periodontally examined and provided saliva, subgingival plaque, and gingival crevicular fluid (GCF) samples. The SCN patients received oral hygiene therapy and are re-evaluated after 6 months. Antimicrobial peptides HPN1-3 and LL-37 are assessed in saliva by ELISA. Concentration of 30 cytokines is measured in saliva and GCF by human 30-plex panel, while bacterial profiles of saliva and subgingival plaque are assessed using 16S rDNA amplicon sequencing. RESULTS: There is no significant difference in salivary HPN1-3 and LL-37 concentration between the SCN patients and controls. At baseline, clinical, immunological, and microbiological parameters of the patients are indicative of oral ecological dysbiosis. The SCN patients have significantly higher bleeding on probing (BOP)%, GCF volume, and cytokine levels, high bacterial load with low bacterial diversity in saliva. The associations between the microbiome and immunological parameters in the SCN patients differ from those in the healthy individuals. CONCLUSIONS AND CLINICAL RELEVANCE: SCN patients have a dysregulated immune response toward commensal oral microbiota, which could be responsible for the observed clinical and microbiological signs of dysbiosis.

Label-Free Quantitative Proteomics versus Antibody-Based Assays to Measure Neutrophil-Derived Enzymes in Saliva.

PURPOSE: This study aims to validate label-free quantitative proteomics (LFQ) against antibody-based methods for quantifying established periodontal disease biomarkers in saliva. EXPERIMENTAL DESIGN: In an experimental gingivitis model, healthy volunteers (n = 10) provide saliva at baseline (d0), during the induction (d7, d14, d21) and resolution (d35) of gingival inflammation (total n = 50). Biomarker levels are analyzed by LFQ and time-resolved immunofluorometric assay (IFMA) or enzyme-linked immunosorbent assay (ELISA). Molecular matrix metalloproteinase (MMP)-8 forms are assessed by Western blot (WB) analysis. RESULTS: LFQ detects significantly (p < 0.05) elevated MMP-8 (d21vsd7, d35vsd7) and tissue inhibitor of matrix metalloproteinases (TIMP)-1 (d35vsd7). Latent MMP-8 (70-80 kDa) is present (d0-d35), but not active MMP-8 (50-60 kDa). LFQ and immunoassay data significantly correlate for MMP-8 (r = 0.36), myeloperoxidase (r = 0.39), polymorphonuclear leukocyte elastase (r = 0.33), and TIMP-1 (r = -0.24). CONCLUSION AND CLINICAL RELEVANCE: LFQ can quantify enzyme levels in saliva, however lacks the ability to measure enzymatic activity. WB analysis reveals that MMP-8 may not be activated during induction of gingival inflammation. Significant but weak correlations between IFMA or ELISA and LFQ suggest a limited capacity of available antibodies to reliably quantify salivary biomarkers for periodontal diseases. Novel "anti-peptide" antibodies designed by newer targeted mass spectrometry-based approaches can help to overcome these drawbacks.

Microbial Analysis of Saliva to Identify Oral Diseases Using a Point-of-Care Compatible qPCR Assay.

Oral health is maintained by a healthy microbiome, which can be monitored by state-of-the art diagnostics. Therefore, this study evaluated the presence and quantity of ten oral disease-associated taxa (P. gingivalis, T. forsythia, T. denticola, F. nucleatum, C. rectus, P. intermedia, A. actinomycetemcomitans, S. mutans, S. sobrinus, oral associated Lactobacilli) in saliva and their clinical status association in 214 individuals. Upon clinical examination, study subjects were grouped into healthy, caries and periodontitis and their saliva was collected. A highly specific point-of-care compatible dual color qPCR assay was developed and used to study the above-mentioned bacteria of interest in the collected saliva. Assay performance was compared to a commercially available microbial reference test. Eight out of ten taxa that were investigated during this study were strong discriminators between the periodontitis and healthy groups: C. rectus, T. forsythia, P. gingivalis, S. mutans, F. nucleatum, T. denticola, P. intermedia and oral Lactobacilli (p < 0.05). Significant differentiation between the periodontitis and caries group microbiome was only shown for S. mutans (p < 0.05). A clear distinction between oral health and disease was enabled by the analysis of quantitative qPCR data of target taxa levels in saliva.

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.