Curr Oral Health Rep https://doi.org/10.1007/s40496-017-0160-0

EPIDEMIOLOGY (M LAINE, SECTION EDITOR)

Proteomics of Periodontal Pocket Dimitra Sakellari 1

# Springer International Publishing AG 2017

Abstract Purpose of Review This study aims to present and evaluate the findings of the literature referring to proteomic analysis of the periodontal pocket aimed to identify potential biomarkers for periodontal disease. Recent Findings A comprehensive examination of data from “shotgun” proteomic analysis has shown that a number of human proteins, previously not extensively investigated in the literature, have emerged as new candidates. Proteins relevant to various biological functions such as actin, profilin, hemoglobin, plastins, alpha-amylase, matrix metalloproteinases, keratins, histones, annexins, antimicrobial peptides including histatins, S-100A9, cathelicidin-related peptide-37 (LL-37), human neutrophil peptides (HNP)-1, -2, and -3, statherin, and cystatins are commonly identified in gingival crevicular fluid (GCF) by proteomic analysis and are upregulated in periodontal disease and therefore could serve as biomarkers. Conclusions Proteomic analysis has provided a new insight into the search for biomarkers of periodontal disease presence, progression, prognosis, and endpoints of treatment. Data derived should be validated by larger scale studies, including significant subject samples. These second-stage studies should focus on evaluating the importance of these proposed new biomarkers using standardized procedures.

This article is part of the Topical Collection on Epidemiology * Dimitra Sakellari [email protected]; [email protected] 1

Department of Preventive Dentistry, Periodontology and Implant Biology, Dental School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Keywords Proteomic analysis . Gingival crevicular fluid . Biomarkers

Introduction Periodontal disease in the forms of gingivitis and periodontitis is widespread and significantly affects both individual welfare and healthcare systems. According to the Global Burden of Disease Study (1990–2015), periodontitis can be considered as the sixth more prevalent disease in the world [1]. It is estimated that around 743 million people are affected by the disease with an overall worldwide prevalence of 11.2% [1, 2]. According to epidemiological data, gingival bleeding is the most prevalent sign of disease, whereas the presence of deep periodontal pockets (≥ 6 mm) varies from 10 to 15% in adult populations [3]. Epidemiological data from the USA suggest that over 64.7 million adults are affected by mild, moderate, and severe forms of periodontitis requiring treatment [4]. Since the prevalence and severity of periodontal disease increases with age, another factor should be taken into account. This factor is the estimated increase in life expectancy as reported by the World Health Organization which suggests that people aged 60 and older make up 12.3% of the global population, and by 2050, that number will rise to almost 22% [5]. Therefore, an important proportion of the population shall require periodontal treatment, which has a significant impact upon escalating public health costs. Indicatively, it has been estimated, that in 2005–2006, 13 million of non-surgical periodontal treatment procedures have been performed in the USA alone, while at the same time frame, one million of periodontal surgical procedures and 15 million maintenance procedures have been provided to patients with periodontitis [6].

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It is also important to report that a link between the presence of periodontitis and negative consequences for general health has also emerged [7–10]. Extensive epidemiological data have shown that periodontitis increases the risk of poor glycemic control in patients with diabetes mellitus as well as diabetes complications and associated morbidity [7]. Data have also shown the relation between periodontitis and the etiopathogenesis of other systemic diseases such as atherosclerotic cardiovascular conditions and nosocomial pulmonary infections through the dissemination of bacteria and inflammatory substances from the periodontal tissues to other locations and activation of the host’s immune mechanisms [7–10]. If such a link will be solidly proven, then treatment of periodontitis will affect not only the oral cavity, but general health as well. Taken collectively, the abovementioned data strongly suggest the need for correct and timely diagnosis of periodontal disease as well as the necessity to fully elucidate pathogenetic mechanisms of conversion from periodontal health to gingivitis and periodontitis. A substantial body of literature has referred to the ability of gingival crevicular fluid (GCF) to act as a diagnostic tool, due to the immediate relativity to periodontal tissues both in healthy and diseased conditions, the complexity of molecules that it contains, the possibility of non-invasive collection, and the potential for site-specific analysis as discussed later in the current review [11–16]. Contemporary science has been greatly influenced and enriched by “omics” technologies. Genomics and metagenomics, proteomics, transcriptomics, and metabolomics have been widely applied in biological sciences—and dentistry—in order to fully describe health and disease conditions and elucidate pathways of disease pathogenesis. In addition, these technological disciplines have been also used to improve clinical diagnostic and prognostic methodology and therapeutics thus to provide reliable biomarkers. Proteomic analysis (proteomics) refers to the identification and quantification of all the proteins and their modifications produced by a unicellular or a multicellular organism at a specific time point of their cellular life [17]. Technological advances and bioinformatics have developed potent analytical tools aimed to investigate mixtures of proteins in human body tissues and fluids in various conditions [18]. Proteomic analysis offers by definition the possibility of a more objective evaluation of components of biological fluids, since pre-selection of proteins to be investigated is not required by researchers and the outcomes of the analysis depend solely on the discriminating ability of the applied technique. During the last decade, this type of analysis has been applied in periodontology creating a new line of research and generating a wealth of data to be interpreted. The aim of the present narrative review is to describe recent findings of the literature referring to proteomic analysis of the

periodontal pocket aimed to identify potential biomarkers for periodontal disease.

Proteomic Analysis in Gingival Crevicular Fluid for Identification of Biomarkers Definition of Biomarkers A biomarker or biological marker is, in general, a substance used as an indicator of a biological state. It is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention [19, 20]. By definition, biomarkers are valuable for early diagnosis, prognosis, and evaluation of the outcomes of a specific treatment modality. In specific, biomarkers can be categorized in six different types and applied for the following functions: 1. Early diagnosis of a disease 2. Diagnosis of presence or absence of a disease. 3. Prognosis of disease progression and possible stratification of patients in order to apply a specialized treatment. 4. Prediction of treatment outcome. 5. Identification of patients who will respond to treatment. 6. “Surrogate “markers instead of clinical evaluation for cessation of treatment [19, 20].

Biomarkers in GCF—an Overview of Data from Non-Proteomic Analysis Since the early 1960s, proteins in the periodontal pocket have been widely investigated as possible biomarkers, as depicted in several relevant reviews [11–16].These numerous studies have mainly used “targeted” approaches of analysis, thus investigated the presence or levels of specific inflammatory mediators or other markers of disease in GCF, mainly by enzymelinked immunosorbent assay (ELISA). Up to 2005, at least 90 such biomarkers have been evaluated including various cytokines, proteolytic enzymes, products of bacterial metabolism, or by-products of periodontal tissue destruction. Evaluation of relevant data has demonstrated that among these potential biomarkers, alkaline phosphatase, b-glucuronidase, and cathepsin-B can offer > 77% precision in predicting periodontal destruction, while matrix metalloproteinases -8 and -9 (MMP-8, MMP-9), neutrophil elastase, and dipeptidyl peptidases II and IV appear to correlate with the presence and/or periodontal disease activity [14, 15]. However, very few of these potential biomarkers have reached clinical praxis as diagnostic chairside kits or as

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convenient laboratory assays with the exception of MMP-8 and its development in point-of-care diagnostics and the attitude of clinicians towards already existing tests is currently unknown [21, 22]. In addition, the inability of a single protein to act as a diagnostic or prognostic biomarker for periodontal disease is suggestive that multiple such proteins should be identified which in combination can act as a reliable biomarker. Newer technologies, like high-throughput immunological assays (e.g., Luminex), have made the simultaneous analysis of multiple biological samples for more proteins—potential biomarkers—feasible, and findings from relevant studies also suggested the need for identification of multiple, and not single, biomarkers which can identify periodontal health from disease with high sensitivity and specificity [23, 24].

based upon their hydrophobicity, vaporized, and then protonated via electrospray ionization. Each peptide is isolated in the mass spectrometer and then fragmented using collisioninduced dissociation to generate a MS/MS spectrum that contains the amino acid composition information of that peptide. The peptide sequence can be derived using either database searches [27], de novo [28, 29], or hybrid de novo/database methods [30]. To determine the protein sequence from the peptide information, a database must be used to match the peptide annotation from the MS/MS spectrum to a theoretically digested peptide from the list of proteins [31, 32]. Once the list of proteins has been identified for a cellular sample, a search for all post-translational modifications (PTMs) may also be performed to identify all PTM types and sites that are present on the proteins [33].

Biomarkers in Gingival Crevicular Fluid—Data from Proteomic Analyses Data from Proteomic Studies for Biomarkers The interpretation of data deriving from proteomic studies, requires a basic description of techniques, usually applied for proteomic analysis. Therefore, in the following section of the present review, a brief overview of how proteomics studies are performed is provided. Proteomic Technologies The cornerstone of proteomic analysis is mass spectrometry, a family of precision techniques offering the possibility to identify and quantify proteins in a complex biological fluids or tissues [25]. Mass spectrometry (MS) is based on ionization of atoms or molecules and measuring of their mass-to-charge ratios. The fundamental components of a mass spectrometer are the ion source, the mass analyzer, and the detector which calculates the abundance of each ion present. The mass analyzer is the core component of the technique and must possess high discriminating ability, sensitivity, and capacity to produce an expanded number of spectra from peptide fragments. Mass spectrometry has been applied combined with gel electrophoresis and, more recently, high-performance liquid chromatography (HPLC).The recognition of spectra and afterwards peptides and proteins is accomplished by means of specialized complex software. Two approaches for proteomic analysis are usually applied: the “top-down” approach refers to isolation of proteins from a complex mixture and analysis of peptide fragments. “Top-down” techniques are currently considered less sensitive. The recognition of proteins is accomplished with the assistance of international free-access databases like, for example, UniprotKB/Swissprot [26], where any researcher can relate his findings with protein sequences already deposited by other researchers in the database and therefore identify them. Using “bottom-up” proteomics, also called “shotgun” approaches, proteins are enzymatically digested, separated

Early Data Various mass spectrometry techniques had been applied until 2010 to identify mainly targeted proteins such as the defensins or the acid-soluble protein content of GCF [34, 35]. Pioneer publications of the field have applied tandem mass spectrometry (MS/MS) techniques to perform large-scale proteomic analysis of GCF, utilizing gel electrophoresis [36] for protein separation or “shotgun” approaches [37•, 38•]. These reports referred to periodontal health or disease [37•, 39] and periodontal patients at maintenance phase [36] or investigated changes during the inflammatory process in an experimental gingivitis model [38•] and have shown an abundance of mainly host-derived proteins in clinical samples. Indicatively, the number of human proteins in GCF identified by these techniques range between 66 and 305. These pioneer studies have provided a wealth of new data regarding the composition of GCF in health and disease and during development of the inflammatory process, reported proteins identified for the first time, and also unraveled a number of possible new biomarkers. These included various antimicrobial peptides, neutrophil defensins, cathepsin, cystatins, annexins, matrix metalloproteinase-8 (MMP-8), L-plastin, actin, and keratins. The authors of the abovementioned studies suggested that far more studies of GCF are required to determine the composition in periodontal health and disease and identify potential biomarkers. At the same time, data from large-scale proteomic analysis have also been reported for saliva, and it has been suggested that the salivary proteome could assist in assessment of periodontal disease activity [40–43]. Although saliva has the advantages of easy collection and handling, and might thus be suitable for large-scale epidemiological studies, gingival crevicular fluid offers more comprehensive

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information regarding the periodontal environment which, in addition, can be site-specific [44, 45]. In addition, salivary biochemistry is extremely complex and can be affected by environmental and/or psychological stimuli [15]. Therefore, it is considered as a “surrogate” fluid for GCF and its versatile biochemistry require cautious interpretation of findings and gingival crevicular fluid remains the biological fluid of choice for studies of periodontal disease. Data from Studies After 2012 After 2012, an increase of the number of relative reports in the literature has been observed for large-scale proteomic analysis of GCF. These reports refer to periodontal health [46], experimental gingivitis [47], chronic periodontitis compared to periodontal health or gingivitis [48, 49, 50•, 51–54], progression of periodontal disease [55], deciduous versus permanent teeth [56], and temporal changes following periodontal treatment [57]. Proteomic studies of GCF before and after 2012 and the number of human proteins identified are shown in Table 1. In order to better synthesize data from these complex studies and extrapolate useful conclusions, this review will not present recent proteomic data referring to periodontal pocket tissues [51], animal models [58], or differences between deciduous and permanent teeth [56]. In accordance with recent reports that apply large-scale genomic, transcriptomic, and metabolomic analysis to investigate periodontal conditions, proteomic analyses, although restricted in number, have demonstrated both the absolute need for integration of these tools in contemporary periodontal

Table 1

research and especially the sometimes unexpected findings that data analysis can generate [59–63]. Differences in methodology regarding collection of clinical samples, site of collection (single or pooled samples), precision, and type of proteomic analysis (quantitative versus qualitative) and different periodontal conditions (health, gingivitis, chronic and aggressive periodontitis, or patients at maintenance phase) included in the studies make comparisons and extrapolation of conclusions rather unsafe at the present time. A general comment regarding findings from proteomic studies in GCF is the complexity of proteins that it contains and the emergence of previously unknown or underestimated molecules to be considered as possible biomarkers related to periodontal conditions. For example, an impressive finding is the fact that consensus “suspects” for periodontal disease pathogenesis and progression, such as pro-inflammatory cytokines, notably the interleukins 1β and 6 (Il-1β and Il-6), or tumor necrosis factor-α (TNF-α),are not among the principal findings of these untargeted approaches. A possible explanation for this rather universal observation is that these important mediators of inflammation in the periodontal pocket are at low abundance compared to highly abundant serum-related proteins in GCF such as albumin or immunoglobulins and therefore masked and not easily identifiable by the current methodologies usually applied for sample processing and analysis [64•]. In accordance with this hypothesis, plasma proteins such as hemoglobin and haptoglobin are frequently detected by proteomic analysis suggesting that although GCF samples visually contaminated with blood are discarded and not processed as reported by authors, the presence of blood cannot be overlooked [47, 48, 50•, 54].

Proteomic studies of the periodontal pocket

Before 2012 Authors

Year of publication Number of subjects

Number of proteins identified Reference

Ngo et al. Bostanci et al. Grant et al. Choi et al. After 2012 Baliban et al. Carneiro et al. Ngo et al. Baliban et al. Bostanci et al. Tsuchida et al. Silva-Boghossian et al. Carneiro et al. Huynh et al.

2010 2010 2010 2011

12 periodontitis at maintenance phase 5 aggressive periodontitis, 5 periodontal health 10 experimental gingivitis 12 chronic periodontitis, 11 periodontal health

66 human 150 (101 human) 202 (186 human) 305 human

[36] [37•] [38•] [39]

2012 2012 2013 2013 2013 2012 2013 2014 2015

6 chronic periodontitis, 6 periodontal health 9 periodontal health 41 periodontitis at maintenance phase 51 chronic periodontitis, 45 periodontal health 20 experimental gingivitis 31 chronic periodontitis, 16 periodontal health 5 chronic periodontitis, 5 periodontal health 40 chronic periodontitis, 40 periodontal health 5 chronic periodontitis, 5 periodontal health 5, gingivitis

462 (432 human) 199 human Prediction model Prediction model 287 (254 human) 619 230 human 291 (238 human) 121 human

[48] [46] [55] [50•] [47] [49] [52] [53] [54]

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Matrix metalloproteinases, already extensively investigated in periodontal pathology and diagnostics, have been identified both in chronic and aggressive periodontitis cases [47, 48] and therefore can be considered solid candidates for integration in diagnostic models. A comprehensive examination of relevant data has shown that a number of human proteins which had not been previously extensively investigated in the literature emerge as new candidates. For example, actin, the major component of microfilaments and actin-related proteins such as actin-binding proteins profilin and plastins, are commonly identified and upregulated in periodontal disease [39, 54]. Alpha-amylase [49], S-100 proteins which are implicated in inflammatory response (especially S-100A9) [48, 53], histones [48, 52, 54], and various other antimicrobial peptides such as cathelicidin, like peptide-37 (LL-37), neutrophil defensin-1, statherin, and cystatins are commonly identified in GCF by proteomic analysis, are generally—but not uniformly—upregulated in periodontal disease and therefore could serve as biomarkers of periodontal conditions. The aforementioned proteins are relevant to various biological functions such as collagen degradation, actin-binding, or antimicrobial activity [57]. Similarly, proteins of bacterial origin have also been identified and/or quantified in GCF using proteomic analysis. As a general finding, the number of bacterial proteins appear to be significantly lower than the number of human proteins, indicating low abundance of these proteins in GCF as well as correct methodology in collecting the clinical sample. However, the biological or diagnostic significance of these proteins cannot be overlooked, since in the case of samples from periodontal disease they usually derive from consensus periodontal pathogens such as Porphyromonas gingivalis [47, 53], Fusobacterium nucleatum [38•, 48], Treponema, and Campylobacter species [48, 50•]. Though certain human proteins, as described above, show strong evidence to indicate their selection as biomarkers, it is not necessarily essential that these proteins are the only candidates. For example, by selecting a combination of proteins to act as biomarkers for the diagnosis of a GCF sample, it is possible that the unexpected presence of a protein in a sample (for example, a periodontitis-related protein found in a sample from periodontal health) would be outweighed by the presence of other proteins that validate the sample diagnosis [50•]. Thus, the benefit from applying advanced algorithms to identify multiple health or disease markers is evident. What remains unclear is what the minimal subset of proteins would be to distinguish a GCF sample as healthy or diseased based solely on the presence/absence or quantitative differences of those proteins. A detailed understanding of the key human and bacterial proteins that are needed for such an analysis would include the

number of human proteins needed, the number of bacterial proteins needed, the number of proteins related to periodontal health, the number of proteins related to periodontal disease, and the relative importance of each of the proteins.

Conclusions Proteomic analysis, especially in the form of the technologically advanced “bottom-up” or “shotgun” analysis (proteomics), has provided a new insight into the search for biomarkers of periodontal disease presence, progression, prognosis, and endpoints of treatment. Similar to all innovative technologies, proteomics appear to follow the “hype cycle” as described for all emerging technologies applied in a number of scientific disciplines (e.g., genomics, metabolomics, and, in our case, proteomics). The five phases of this cycle are generally described as technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and peak of productivity. In simple terms, the introduction of high-throughput proteomic analysis in the 2010s has gained significant scientific interest which has led to more scientific studies after 2012, expecting to provide reliable biomarkers that the periodontal community is looking for, since the 1960s. The wealth of data provided so far remains to be properly evaluated in order to reach the slope of enlightenment and productivity phase. Therefore, the unbiased, untargeted data derived from relevant studies should be validated by larger scale studies, including significant subject samples. These second-stage studies can focus on estimating the importance of these proposed new biomarkers by standardized procedures. High-sensitivity and high-specificity enzyme linked immunosorbent assays (ELISA) and mass spectral techniques such as selected or multiple reaction monitoring (SRM and MRM) could evaluate the utility and clinical application of results from high-throughput proteomic studies. This aspect might have to be addressed by all teams involved in this novel approach, in order to achieve indicative proposals for clinicians and connect the current trend in scientific results with clinical periodontal praxis, regarding diagnosis, prognosis, and clinical endpoint of periodontal treatment. Compliance with Ethical Standards Conflict of Interest interest.

The author declares that he has no conflict of

Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.

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