Toward the definition of a peptidome signature and protease profile in chronic periodontitis

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.

Single-Particle Plasmon Sensor to Monitor Proteolytic Activity in Real Time

We have established a label-free plasmonic platform that monitors proteolytic activity in real time. The sensor consists of a random array of gold nanorods that are functionalized with a design peptide that is specifically cleaved by thrombin, resulting in a blueshift of the longitudinal plasmon. By monitoring the plasmon of many individual nanorods, we determined thrombin's proteolytic activity in real time and inferred relevant kinetic parameters. Furthermore, a comparison to a kinetic model revealed that the plasmon shift is dictated by a competition between peptide cleavage and thrombin binding, which have opposing effects on the measured plasmon shift. The dynamic range of the sensor is greater than two orders of magnitude, and it is capable of detecting physiologically relevant levels of active thrombin down to 3 nM in buffered conditions. We expect these plasmon-mediated label-free sensors to open the window to a range of applications stretching from the diagnostic and characterization of bleeding disorders to fundamental proteolytic and pharmacological studies.

Salivary peptidome analysis and protease prediction during orthodontic treatment with fixed appliances

Orthodontic tooth movement (OTM) occurs through proteolytic remodelling within the periodontium following the application of external force to the tooth. This study describes the first characterization of the salivary peptidome and protease profile during the alignment stage of fixed appliance orthodontic treatment. Unstimulated whole mouth saliva from 16 orthodontic patients (10 males, 6 females, mean (SD) age 15.2 (1.6) years) was collected prior to fixed appliance placement (T1), 1-h (T2), 1-week (T3) following fixed appliance placement and on completion of mandibular arch alignment (T4). Salivary peptides were extracted using filtration followed by mass spectrometry to identify amino acid sequences. Protease prediction was carried out in silico using Proteasix and validated with gelatin zymography and enzyme-linked immunosorbent assay. A total of 2852 naturally-occurring peptides were detected, originating from 436 different proteins. Both collagen and statherin-derived peptide levels were increased at T2. Proteasix predicted 73 proteases potentially involved in generating these peptides, including metalloproteinases, calpains and cathepsins. Changes in predicted activity of proteases over time were also observed, with most metalloproteinases showing increased predicted activity at T2-T3. Increased gelatinolytic activity and MMP8/MMP9 levels were detected at T3. Collectively, multiple protein targets and changes in protease-predicted activity during OTM have been identified.

The oral microbiome: Role of key organisms and complex networks in oral health and disease

States of oral health and disease reflect the compositional and functional capacities of, as well as the interspecies interactions within, the oral microbiota. The oral cavity exists as a highly dynamic microbial environment that harbors many distinct substrata and microenvironments that house diverse microbial communities. Specific to the oral cavity, the nonshedding dental surfaces facilitate the development of highly complex polymicrobial biofilm communities, characterized not only by the distinct microbes comprising them, but cumulatively by their activities. Adding to this complexity, the oral cavity faces near-constant environmental challenges, including those from host diet, salivary flow, masticatory forces, and introduction of exogenous microbes. The composition of the oral microbiome is shaped throughout life by factors including host genetics, maternal transmission, as well as environmental factors, such as dietary habits, oral hygiene practice, medications, and systemic factors. This dynamic ecosystem presents opportunities for oral microbial dysbiosis and the development of dental and periodontal diseases. The application of both in vitro and culture-independent approaches has broadened the mechanistic understandings of complex polymicrobial communities within the oral cavity, as well as the environmental, local, and systemic underpinnings that influence the dynamics of the oral microbiome. Here, we review the present knowledge and current understanding of microbial communities within the oral cavity and the influences and challenges upon this system that encourage homeostasis or provoke microbiome perturbation, and thus contribute to states of oral health or disease.

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.

Predicted salivary human protease activity in experimental gingivitis revealed by endoProteo-FASP approach

Gingivitis is a highly prevalent oral condition that can be studied in humans via the 21-d experimental gingivitis model, which allows for investigations into the induction and resolution of gingivitis. In this study, we used the autolysis of saliva as a source of peptides to predict the activity of human proteases in saliva during induction and resolution of inflammation. Healthy volunteers, with no remarkable oral or systemic conditions, were recruited into the study and stimulated saliva samples were collected at days 0, 21, and 35 of experimental gingivitis. Plaque and gingival indices were recorded to ensure clinical induction and resolution. Saliva was auto-digested at 37°C for 18 h before identification of peptides by mass spectrometry. Protease prediction was carried out using Proteasix in silico with the identified peptides. A comparison of day 0 to days 21 and 35 showed changes in predicted protease activity. Correlation network analysis revealed that at day 21 the proteases became less connected and showed a potential for a dysregulated system; by day 35 the connectivity was returning towards similar conditions at day 0. This study demonstrates that changes in predicted proteases are apparent even in saliva collected from donors experiencing inflammation around three teeth.

Biochemical analysis of oral fluids for disease detection

The field of diagnostics using invasive blood testing represents the majority of diagnostic tests used as part of routine health monitoring. The relatively recent introduction of salivary diagnostics has lead to a major paradigm shift in diagnostic analyses. Additionally, in this era of big data, oral fluid testing has shown promising outcomes in a number of fields, particularly the areas of genomics, microbiomics, proteomics, metabolomics, and transcriptomics. Despite the analytical challenges involved in the interpretation of large datasets generated from biochemical studies involving bodily fluids, including saliva, many studies have identified novel oral biomarkers for diagnosing oral and systemic diseases. In this regard, oral biofluids, including saliva, gingival crevicular fluid (GCF), peri-implant crevicular fluid (PICF), dentinal tubular fluid (DTF), are now attracting increasing attention due to their important attributes, such as noninvasive sampling, easy handling, low cost, and more accurate diagnosis of oral diseases. Recently, the utilization of salivary diagnostics to evaluate systemic diseases and monitor general health has increased in popularity among clinicians. Saliva contains a wide range of protein, DNA and RNA biomarkers, which assist in the diagnosis of multiple diseases and conditions, including cancer, cardiovascular diseases (CVD), auto-immune and degenerative diseases, respiratory infections, oral diseases, and microbial (viral, bacterial and fungal) diseases. Moreover, due to its noninvasive nature and ease-of-adoption by children, it is now being used in mass screening programs, oral health-related studies and clinical trials in support of the development of therapeutic agents. The recent advent of highly sensitive technologies, such as next-generation sequencing, mass spectrometry, highly sensitives ELISAs, and homogeneous immunoassays, suggests that even small quantities of salivary biomarkers are able to be assayed accurately, providing opportunities for the development of many future diagnostic applications (including emerging technologies, such as point-of-care and rapid molecular technologies). The present article explores the omics and biochemical compositions of various oral biofluids with important value in diagnostics and monitoring.

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.

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.

Proteomic analysis of whole saliva in chronic periodontitis

Periodontitis is a chronic inflammatory disease resulting from a dysbiosis of the dental biofilm and a dysregulated host response in susceptible individuals. It is characterized by periodontal attachment destruction, bone resorption and eventual tooth loss. Salivary biomarkers have been sought to predict and prevent periodontitis. This comparative study analyzed the salivary proteome of individuals with chronic periodontitis (CP) and periodontal health (PH) and correlated specific proteins with clinical parameters of disease by using mass spectrometry. Stimulated whole saliva was obtained 10 PH and 30 CP patients and pooled into 5 healthy control samples and 15 CP samples. After precipitation with TCA, samples were digested enzymatically with trypsin and analyzed by a LTQ Orbitrap Velos equipped with a nanoelectrospray ion source. A wide range of salivary proteins of various functions was significantly reduced in CP individuals, whereas salivary acidic proline-rich phosphoprotein, submaxillary gland androgen-regulated protein, histatin-1, fatty acid binding protein, thioredoxin and cystatin-SA were predominant in diseased patients and correlated significantly with signs of periodontal attachment loss and inflammation. In conclusion, few specific salivary proteins were associated with CP. These findings may contribute to the identification of disease indicators or signatures for the improvement of periodontal diagnosis. SIGNIFICANCE: Periodontitis is a chronic inflammatory disease that results in periodontal attachment destruction, bone resorption and eventual tooth loss. Salivary biomarkers have been sought to predict periodontitis. The analysis of the salivary proteome of individuals with chronic periodontitis indicated that several proteins of various functions were significantly reduced in these individuals, except for salivary acidic proline-rich phosphoprotein, submaxillary gland androgen-regulated protein, histatin, fatty acid binding protein, thioredoxin and cystatin. Differences in salivary proteome profiles between periodontal health and periodontitis may contribute to the identification of disease indicators and to the improvement of periodontal diagnosis and treatment.

Degradomics in Biomarker Discovery

The upregulation of protease expression and proteolytic activity is implicated in numerous pathological conditions such as neurodegeneration, cancer, cardiovascular and autoimmune diseases, and bone degeneration. During disease progression, various proteases form characteristic patterns of cleaved proteins and peptides, which can affect disease severity and course of progression. It has been shown that qualitative and quantitative monitoring of cleaved protease substrates can provide relevant prognostic, diagnostic, and therapeutic information. As proteolytic fragments and peptides generated in the affected tissue are commonly translocated to blood, urine, and other proximal fluids, their possible application as biomarkers is the subject of ongoing research. The field of degradomics has been established to enable the global identification of proteolytic events on the organism level, utilizing proteomic approaches and sample preparation techniques that facilitate the detection of proteolytic processing of protease substrates in complex biological samples. In this review, some of the latest developments in degradomic methodologies used for the identification and validation of biologically relevant proteolytic events and their application in the search for clinically relevant biomarker candidates are presented. The current state of degradomics in clinics is discussed and the future perspectives of the field are outlined.

Rapid Detection and Identification of Antimicrobial Peptide Fingerprints of Nasal Fluid by Mesoporous Silica Particles and MALDI-TOF/TOF Mass Spectrometry: From the Analytical Approach to the Diagnostic Applicability in Precision Medicine

Background: Antimicrobial peptides (AMP) play a pivotal role in innate host defense and in immune response. The delineation of new MS-based profiling tools, which are able to produce panels of AMP of the nasal fluid (NF), may be attractive for the discovery of new potential diagnostic markers of respiratory disorders. Methods: Swabs collected NF from healthy patients and from patients with respiratory disorders. We used a fast procedure based on mesoporous silica particles (MPS) to enrich NF in its AMP component in combination with MALDI-TOF/TOF MS as a key tool for rapidly analyzing clinical samples. Results: Reproducible MS peptide fingerprints were generated for each subject and several AMP were detected including (Human Neutrophil Peptides) HNPs, Statherin, Thymosin-β4, Peptide P-D, II-2, β-MSP, SLPI, Lysozyme-C, and their proteo-forms. In particular, Statherin, Thymosin-β4, and Peptide P-D were accurately identified by direct MS/MS sequencing. Examples of applicability of this tool are shown. AMP fingerprints were obtained before and after a nasal polypectomy as well as before and post-treatment with azelastine/fluticasone in one case of allergic rhinitis. Conclusion: The potential of our platform to be implemented by new mesoporous materials for capturing a wider picture of AMP might offer an amazing opportunity for diagnostic clinical studies on individual and population scales.

EndoProteoFASP as a Tool to Unveil the Peptidome-Protease Profile: Application to Salivary Diagnostics

In the quest to fully comprehend the proteolytic events leading to the generation of the salivary peptidome, we have developed a method for the sequential elution of salivary peptides throughout progressive endogenous proteolysis. By screening the time-dependent changes in the salivary peptidome we can predict the activity pattern of salivary proteases responsible for such peptide fingerprint and identify susceptible protein targets. Herein, we describe a step-by-step tutorial based on a filter-aided sample preparation (FASP) method, taking advantage of the endogenous salivary proteases armamentarium (endoProteoFASP), to produce new peptides from the salivary proteins, adding to those present in the sample at the time of collection. In this protocol, the different sets of peptides retrieved after sample elution are identified following a liquid chromatography-tandem mass spectrometry approach. The likelihood of a large set of endogenous proteases (collected from several public sources) to be responsible for the generation of such peptides can be predicted by the analysis of the cleavage site specificity by Proteasix ( http://proteasix.cs.man.ac.uk /) algorithm. The attained peptidome-protease profile can be useful to elucidate the peptidome dynamics and the proteolytic events underpinning pathophysiological phenomena taking place locally within the oral cavity. This may help clinicians to diagnose oral pathologies and develop preventive therapeutic plans.

A fractionation approach applying chelating magnetic nanoparticles to characterize pericardial fluid's proteome

Owing to their close proximity, pericardial fluid (PF)'s proteome may mirror the pathophysiological status of the heart. Despite this diagnosis potential, the knowledge of PF's proteome is scarce. Large amounts of albumin hamper the characterization of the least abundant proteins in PF. Aiming to expand PF's proteome and to validate the technique for future applications, we have fractionated and characterized the PF, using N-(trimethoxysilylpropyl)ethylenediamine triacetic acid (EDTA)-functionalized magnetic nanoparticles (NPs@EDTA) followed by a GeLC-MS/MS approach. Similarly to an albumin-depletion kit, NPs@EDTA-based fractionation was efficient in removing albumin. Both methods displayed comparable inter-individual variability, but NPs@EDTA outperformed the former with regard to the protein dynamic range as well as to the monitoring of biological processes. Overall, 565 proteins were identified, of which 297 (>50%) have never been assigned to PF. Moreover, owing to this method's good proteome reproducibility, affordability, rapid automation and high binding ability of NP@EDTA, it bears a great potential towards future clinical application.

Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy

Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution ¹H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis.

EDTA-functionalized magnetic nanoparticles: A suitable platform for the analysis of low abundance urinary proteins

Urine is a highly attractive source of biological information and disease biomarkers, whose proteome characterization is ongoing. To that end, depletion/enrichment strategies for protein analysis can be of great convenience. We have thus developed a method based on the use of EDTA-functionalized magnetic nanoparticles (NPs@EDTA), to fractionate urine samples before liquid chromatography-mass spectrometry analysis and compared the identified proteins with those obtained from ultrafiltrated/unfractionated (UF) urine samples. NPs@EDTA allowed larger urine volumes to be processed, resulting in a greater number of protein identifications (~2-fold) at a lower cost when compared to UF samples. Proteins of greater abundance (such as albumin and uromodulin) were, at least partially, depleted with NPs@EDTA while those of lower abundance were enriched. Bioinformatics analysis showed that approximately 27% of NPs@EDTA-enriched proteins were annotated as displaying enzymatic activity, most of these being hydrolytic enzymes (56%), particularly proteases/peptidases (48%). Also, post-translational modifications were prominently predicted across NPs@EDTA-enriched proteins (90%), particularly glycosylation (52%), phosphorylation (47%) and acetylation (30%). NPs@EDTA allowed the identification of 109 proteins in urine for the first time, showing high potential as a platform for urine's fractionation prior to proteomic analysis.

Prediction of Proteases Involved in Peptide Generation

Clinical proteomics has led to the identification of a substantial number of disease-associated peptides and protein fragments in several conditions such as cancer, kidney, or cardiovascular diseases. In silico prediction tools that can facilitate linking of identified peptide biomarkers to predicted protease activity might therefore significantly contribute to the understanding of pathophysiological mechanisms of these diseases. Proteasix is an open-source, peptide-centric tool that can be used to predict in silico the proteases involved in naturally occurring peptide generation. From an input peptide list, Proteasix allows for automatic cleavage site reconstruction and protease associations.

Longitudinal quantification of the gingival crevicular fluid proteome during progression from gingivitis to periodontitis in a canine model

Aim

Inflammatory periodontal disease is widespread in dogs. This study evaluated site‐specific changes in the canine gingival crevicular fluid (GCF) proteome during longitudinal progression from very mild gingivitis to mild periodontitis. Periodontitis diagnosis in dogs requires general anaesthesia with associated risks and costs; our ultimate aim was to develop a periodontitis diagnostic for application in conscious dogs. The objective of this work was to identify potential biomarkers of periodontal disease progression in dogs.

Material and Methods

Gingival crevicular fluid was sampled from a total of 10 teeth in eight dogs at three different stages of health/disease and samples prepared for quantitative mass spectrometry (data available via ProteomeXchange; identifier PXD003337). A univariate mixed model analysis determined significantly altered proteins between health states and six were evaluated by ELISA.

Results

Four hundred and six proteins were identified with 84 present in all samples. The prevalence of 40 proteins was found to be significantly changed in periodontitis relative to gingivitis. ELISA measurements confirmed that haptoglobin was significantly increased.

Conclusions

This study demonstrates for the first time that proteins detected by mass spectrometry have potential to identify novel biomarkers for canine periodontal disease. Further work is required to validate additional biomarkers for a periodontitis diagnostic.