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

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Review

Proteomics for the discovery of biomarkers and diagnosis of periodontitis: a critical review Expert Rev. Proteomics 11(1), 31–41 (2014)

Yannis A Guzman1, Dimitra Sakellari2, Minas Arsenakis3 and Christodoulos A Floudas*1 1 Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA 2 Department of Preventive Dentistry, Periodontology and Implant Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece 3 Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece *Author for correspondence: Tel.: +1 609 258 4595 Fax: +1 609 258 0211 [email protected]

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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. KEYWORDS: 2D gel electrophoresis • aggressive periodontitis • biomarkers • chronic periodontitis • gingivitis • gingival crevicular fluid • shotgun proteomics • whole saliva

Periodontitis is an inflammatory disease of the periodontium that has been highly correlated with the presence of specific bacterial communities [1], though the detailed pathogenetic mechanisms of periodontitis remain unknown. Growing evidence indicates that the progression to periodontitis involves a shift of the oral microbial population away from a healthy homeostasis [2], and imbalanced host inflammatory responses to increased populations of bacteria can result in attachment loss of teeth from the alveolar (jaw) bone and alveolar bone resorption [3]. The inflammatory host environment activates proinflammatory cytokines and latent matrix metalloproteases (MMPs) which degrade collagen in the inflamed gingival tissue and lead to the formation of periodontal pockets where gingival tissue meets the tooth surface [4], which in turn can harbor a large and varied population of bacteria that further initiates host defense mechanisms. Periodontitis has been deemed as a ‘complex’ disease with a multi-faceted cascade of events in disease onset and disease progression influenced by environmental, systemic (e.g., diabetes mellitus) and genetic risk factors [5]; the correlation of periodontitis as a risk factor for various systemic 10.1586/14789450.2014.864953

conditions such as chronic cardiovascular diseases or the birth of low-weight premature babies has been extensively investigated. Gingivitis is an inflammatory periodontal disease that is a precursor to periodontitis, but the progression of gingivitis to periodontitis is not easily predicted through clinical measures and can vary by individual and even by site in the same patient. Two common and distinct forms of periodontitis are chronic periodontitis (CP) and aggressive periodontitis (AgP), both of which can be further characterized as localized or generalized [6]. Differences in clinical features, etiology and associated microbiology have distinguished localized and generalized AgP (LAgP and GAgP, respectively) as distinct diseases [7,8]. However, the clinical distinction between CP and AgP can still be ambiguous, with the picture clouded by seemingly identical histopathology and the possibility of disease superposition [9]. The clearest bacterial correlation to disease state may be the presence of Aggregatibacter actinomycetemcomitans in cases of LAgP, but this has not been observed in all cases [8]. Bleeding on probing can indicate disease activity with high sensitivity

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Guzman, Sakellari, Arsenakis & Floudas

but low specificity, while other diagnostic measures such as clinical attachment level help to indicate the presence of disease and its severity but provide little on the progression of the disease or the efficacy of the treatment program [10,11]. Biomarkers are measurable characteristics that can act as reliable indicators of biological processes or pharmacological responses to treatment, and can be applied to the diagnosis, staging or prognosis of the disease; modern usage of the term often implies molecular biomarkers. The complexity of periodontitis has led to the extensive search of clinically applicable biomarkers for all phases of disease treatment. However, with the exception of MMP-8 and its development in point-of-care diagnostics [12], such biomarkers have not been extensively applied in clinical praxis. From disease onset to clinical endpoint, robust biomarkers would have clinical application and benefit in the following aims [10,11,13]: 1. Early risk-assessment and screening of patients with environmental or behavioral risk-factors; 2. Prediction of transition from gingivitis to periodontitis; 3. Early indication of disease onset; 4. Factors for differential diagnoses; 5. Temporal indications of disease activity for disease severity, staging and branching treatment courses; 6. Differentiation of actively progressing and inactive periodontal sites and prognoses for future tissue destruction; 7. Monitoring patient response to treatment programs and indications of disease end point. The successful discovery and utilization of biomarkers to the aforementioned areas involving early diagnoses, the prediction of future tissue destruction and temporal profiling would be of particular value and would allow practitioners to tailor treatment programs or branch therapeutic interventions based on predictive and time-sensitive information. The protein domain holds considerable potential as a source for biomarkers due to the close relationship between the quantitative and temporal expression of the static genome and biological state and activity. Modern mass spectrometry (MS)-based proteomics experiments enable the study and discovery of the interconnectivity between proteome and clinical state, and are particularly well-suited for the study of multicomponent complex pathologies such as that of periodontitis across host defense responses and the activity of suspected pathogens of bacterial, fungal and viral origin. TABLE 1 presents 17 proteomics studies, to date, on periodontitis (with gingivitis included for aim two) that utilize different biological mediums and MS platforms, both of which represent experimental design decisions that will impact results. This review discusses considerations for the experimental design of a proteomics study on periodontitis, extracts and outlines common candidate biomarkers among prior results, and discusses the progress and potential of this active domain of clinical proteomics. 32

Selection of sample medium

Because of the high complexity of biological tissues, clinical proteomics studies typically analyze biofluids such as urine or cerebrospinal fluid. Research in periodontal biomarkers has included such biological media as saliva, serum, subgingival plaque, gingival tissue and gingival crevicular fluid (GCF) [14], and with the exception of one study that harvested interproximal gingival tissues [15], TABLE 1 reveals that proteomics studies on periodontitis have featured either whole saliva or GCF as the sample medium. Both saliva and GCF can be noninvasively collected and thus specific findings can translate easily to a chairside diagnostic tool. GCF is particularly relevant, as it transforms in periodontal disease from a gingival transudate fluid to an exudate that reflects serum composition and contains substances from structural tissues of the periodontium and the bacterial population of the gingival pocket [16]. Analysis of GCF can be site-specific and can enable healthy controls from the same patient, as well as elucidate differences in disease progression by site. Many studies in TABLE 1 chose to pool GCF site samples by subject prior to analysis, which results in a loss of site-specific information but may also result in a more robust proteomic fluid that emphasizes key low-abundance proteins present in many or all diseased/healthy sites [17]. Saliva has been the subject of many protein biomarker studies for a variety of oral and non-oral diseases and represents an alternative medium to GCF [18]. The contribution of GCF to saliva allows whole saliva to act as a pooled surrogate fluid to GCF. However, it should be noted that GCF contributes a small percentage of the total protein content of saliva, with the vast majority (>90%) of salivary proteins originating from the parotid, submandular and sublingual glands [19], many of which are glycosylated or phosphorylated (intracellularly or intraductally) [20]. The analysis of glycosylated proteins is an emerging subfield in clinical proteomics which requires special experimental considerations (see [21,22] for review). From a practical view, saliva can be collected by non expert staff and quickly provides an overall appraisal of the entire oral environment. By comparison, GCF sampling is more technically demanding, and with up to 168 possible sampling sites, rapid whole-mouth evaluation is infeasible [4,14]. The utilization of saliva requires careful consideration in proteomics studies. While not as pronounced as serum, saliva does exhibit a dynamic range of protein abundances, with estimates varying on the severity of the problem (from the top 10 proteins contributing 40% of the total protein content [23] to the top 10 protein classes contributing 98% [24]). Large ranges of protein abundances complicate the identification of low-abundance proteins and may require very sensitive proteomics platforms (see the following section) or protein depletion steps [25], which in turn may perturb the sample. Saliva also contains a large number of proteases, varied in activity, which can destroy potential protein biomarkers; instead of adding protease inhibitors, which do not completely halt proteolysis, samples should be fast frozen and, applicable to almost all proteomics experiments, must be stored at -80˚C [26,27]. Saliva collection and subject selection must also Expert Rev. Proteomics 11(1), (2014)

Proteomics for diagnosis of periodontitis

Review


Table 1. Clinical proteomics studies of periodontal disease. Study (year)

Disease

Sample medium

Subjects (n)

Samples (n)

MS platform

Proteins identified by MS (n)

Ref.

Wu et al. (2009)

GAgP

WUS

5 diseased, 5 healthy

10

2-DE, LC-MS/MS

11 human

[84]

Ngo et al. (2010)

CP

GCF

12 maintenance-phase

30 individual sites, 3 pooled samples

A. 1-DE, MALDI-TOF/TOF B. 1-DE, LC-MS/MS

66 human (23 from A, 66 from B)

[40]

Bostanci et al. (2010)

GAgP

GCF


10 pooled (4 sites/sample)

LC-MSE

150 (101 human, 27 bacterial, 14 yeast, 8 viral)

[34]

Haigh et al. (2010)

Unclassified severe generalized periodontitis

WSS

9 diseased (pre/post treatment)

18

A. 2-DE, MALDI-TOF B. 2-DE, LC-MS/MS


[39]

Gonçalves et al. (2010)

CP

WUS


20

A. 2-DE, MALDI-TOF/TOF B. LC-MS/MS


[71]

Grant et al. (2010)

Gingivitis†

GCF

10 healthy (test/ control; experimental model)

8 pooled (3 sites/sample; pooled across subjects)

LC-MS/MS

202 (186 human, 16 bacterial)

[47]

Gonçalves et al. (2011)

Gingivitis

WUS


20

A. 2-DE, MALDI-TOF/TOF B. LC-MS/MS


[70]

Choi et al. (2011)

CP

GCF


4 pooled across subjects and condition

1-DE LC-MS/MS

305 human

[50]

Baliban et al. (2012)

CP

GCF


12 pooled (4 sites/sample)

LC-MS/MS

432 human, 32 bacterial

[44]

Kido et al. (2012)

Unclassified periodontitis

GCF


3 pooled (2 diseased, 1 healthy)‡

1-DE LC-MS/MS

231 human

[48]

Range´ et al. (2012)


WSS

13 diseased (obese), 25 healthy (obese), 19 healthy (non-obese)

57 (samples pooled for 1-DE MS)

SELDI-TOF, 1-DE MS or MALDI-TOF/TOF

7 human

[94]

Ngo et al. (2013)

CP

GCF

41 maintenance-phase

41 individual sites

MALDI-TOF/TOF

Prediction model

[68]

Baliban et al. (2013)

CP

GCF


96 pooled (4 sites/ sample)

LC-MS/MS

Prediction model

[45]

Bertoldi et al. (2013)

CP

IPG

25 diseased (at least one intra-bony defect)

50 (pocketassociated/ healthy per subject)

2-DE, LC-MS/MS

19 human

[15]

Bostanci et al. (2013)

Gingivitis

GCF

20 healthy (test/ control; experimental model)

80 (2 sites/sample)

LC-MS/MS

254 human, 18 bacterial

[17]

Tsuchida et al. (2013)

CP

GCF


6 pooled (4 diseased, 2 healthy)‡

LC-MS/MS

619 human

[49]

Gesell Salazar et al. (2013)


WSS


40

LC-MS/MS

344 human

[46]

†

Proteomics studies on gingivitis included for aim 2. Some samples reserved for western blotting. 1-DE or 2-DE: One-(two-)dimensional electrophoresis; CP: Chronic periodontitis; GAgP: Generalized aggressive periodontitis; GCF: Gingival crevicular fluid; IPG: Interproximal gingival tissues; LC: Liquid chromatography; MALDI: matrix-assisted laser desorption/ionization; MS/MS: Tandem mass spectrometry; SELDI: Surfaceenhanced laser desorption/ionization; WSS: whole stimulated saliva; WUS: whole unstimulated saliva. ‡


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be carefully controlled, as the relative and absolute protein composition of saliva can change due to many factors such as flow rate, elapsed time from a flow rate increase due to stimulation and circadian rhythms [28]. Age, in particular, may alter the composition and concentration of the salivary proteome [29,30]. In contrast, GCF volume, which increases in periodontal disease, did not statistically correlate with protein concentration when controlled by disease [31], and protein concentration did not differ between ages 12–69 [32] but may differ between younger subjects and adults [33]. Studies in TABLE 1 are divided between utilizing whole unstimulated saliva samples versus whole stimulated saliva samples; these studies should not be quantitatively compared, as increases in saliva flow rate due to stimulation and the method of stimulation will alter the contributions of the major salivary glands and thus the abundances of proteins heavily contributed by specific glands [28]. Additionally, the duration of stimulation and subject exercise patterns can affect whole stimulated saliva composition. Studies tend to justify the selection of one biofluid over the other in an absolute sense, but future studies can utilize both GCF and saliva synergistically and in tandem in contributing to the aforementioned aims of biomarker application in periodontitis. GCF displays stability and specificity, both to the disease and by site, and is well-suited to examining various causes of the pathogenesis of periodontitis. GCF will likely remain a more natural choice for research into applications as a complement to clinical features for disease diagnosis and classification, and for site-specific disease progression predictions and monitoring patient response to the therapy. GCF can also enable intra-patient healthy controls to combat inter-patient variability, a phenomenon which has been observed, to some extent, in GCF samples of the same group [34] and to a high degree in saliva samples from healthy subjects [35]. Whole saliva is the clear choice for early patient screenings, large-scale population sampling and avoiding heavy sampling in the study of the transition from gingivitis to periodontitis for predictive cascade triggers. Whole saliva, particularly whole unstimulated saliva, might also be utilized qualitatively in disease diagnosis and post-diagnosis patient monitoring for biomarker proteins that are highly abundant in GCF and as a means of rapid information gathering in generalized cases of periodontitis. Experimental workflow

The proteomics platforms utilized in the studies of TABLE 1 can be classified by the separations technique utilized, specifically as: Category (a) by-protein analyses with separation by gel electrophoresis (usually 2DE) with spot excision and MS, typically matrix-assisted laser desorption/ionization (MALDI)-TOF or digestion and tandem MS (MS/MS, i.e., multiple MS scans with precursor ion selection and fragmentation); and Category (b) digestion, possibly following fractionation, with highthroughput liquid chromatography (LC) as the primary method of separation, followed by ESI and MS/MS (i.e., bottom-up 34

‘shotgun’ LC-MS/MS). Category (a) represents the classical proteomics platform and was applied by most of the early studies of TABLE 1, as 2DE is well-suited to providing rapid global protein profiles between diseased and healthy groups. Category (b) has risen in prominence in periodontal studies and clinical proteomics in general over time. For identifying protein isoforms or high-abundance variations of a post-translationally modified protein, protein separations by 2DE remains a strong alternative or complement to shotgun platforms [36]; analyses with a bottom-up protocol rely on post-translational modifications (PTMs) to be located on proteotypic peptides (i.e., those consistently selected and identified by MS/MS) for identification of proteins with PTMs and recognition of abundance differences between modified and unmodified forms. Differential quantification via 2DE proceeds in a straightforward manner through the comparison of staining intensities, though the process is prone to serious bias, obfuscation of low-abundance proteins by high-abundance proteins and migration loss of very small or highly acidic/basic proteins [19]. The analysis of increasingly complex biological samples will increase the probability of co-located proteins and experimental variability, leading to quantification errors [37]. Certain protein classes can exhibit anomalous interactions with staining substances, and have been shown to be more reliably detected, isoforms and PTMs included, using LC-ESI than by 2DE-MALDI [38]. The common pairing of gel electrophoresis with ionization via MALDI versus ESI represents an experimental design decision; two studies in TABLE 1 [39,40] paired gel electrophoresis with both MALDI and LC-ESI, and both identified more proteins using LC separations. The comparison may not be quite fair, as the former utilized 1DE as a preliminary separations technique and thus LC-ESI likely benefited from the additional separations step, while the latter utilized MALDI-MS but tandem MS with LC. Nevertheless, Ngo et al. [40] observed the high sensitivity of the employed nano-LC-ESI-MS/MS platform and avoided implementing the LC-MALDI-TOF/TOF platform due to low throughput. MALDI behavior can also be strongly dependent on matrix interactions and mixture composition and the resulting spectral intensities can be independent of peptide abundance [19]. The increased manual labor, decreased throughput and increased cost associated with 2DE separations has resulted in the increased prevalence of shotgun platforms and their successful application to biological samples of high complexity [41]. The platform can accomplish the rapid, unbiased characterization and discovery of a proteome with very high sensitivity, depth and simplicity [42,43]. With the exception of one group, the numbers of proteins identified by the studies in TABLE 1 that employed shotgun proteomics platforms were an order of magnitude higher than those from 2DE platforms [17,34,44–50]. In contrast with 2DE platforms, quantification is made after MS analysis, making the determination of accurate differential protein abundances less straightforward. MS quantification methods often utilize isotopic labeling techniques and are thus categorized as either label or label-free methods (see [51] for a Expert Rev. Proteomics 11(1), (2014)



thorough review). Grant et al. [47] utilized the isobaric tags for relative and absolute quantification strategy (iTRAQ, [52]) and Tsuchida et al. [49] utilized, tandem mass tags (TMTs, [53]); both methods are multiplexed isobaric labeling strategies that are accurate, but limited to the comparison of up to eight samples and are thus poorly applicable to large-scale validation studies. Even small-scale biomarker studies must consider the increased time, labor and cost associated with utilizing labeling techniques. The rest of the shotgun proteomics experiments employed label-free techniques (i.e., quantification only utilizing MS data and no external additives) that are theoretically unlimited in the number of samples that can be compared and are applicable to the largest dynamic range. However, label-free quantification consists of a large subfield of algorithmic methods and techniques with varying degrees of accuracy and robustness [54]. Stable application of label-free quantification requires robust and high-performance bioinformatics platforms; the complex data output from shotgun proteomics places a more stringent requirement on the accuracy and stability of optimization-based and database-driven algorithms, especially those for peptide identifications [55–58] and their attribution, specifically degenerate peptide identifications (i.e., the protein inference problem [59]), to protein identifications [55,56,60–64]. Researchers must consider the exacerbations shotgun proteomics places on the limitations of MS/MS, specifically datadependent acquisition (DDA)-mode MS/MS, where only the highest-abundance peptide precursor ions per scan are selected for fragmentation and are able to be identified, while low-intensity precursor ions of certain peptides may never be selected. This may lead to undersampling of the proteome and missed detections of low-abundance, possibly differential proteins; those detected in one clinical group but not in the other are not necessarily absent, but may be downregulated and, due to co-eluting higher-intensity peptide precursors, not being selected for fragmentation. However, the limitations and reproducibility issues of DDA are improved upon with technological improvements in separations platforms and modern, high-resolution, high-sampling speed MS instruments, while data-independent acquisition techniques are also under development [65,66] and were employed by Bostanci et al. [34]. A promising resolution to these limitations, and one that can be appended to those results obtained by shotgun proteomics platforms in TABLE 1, is the application of a multiple reaction monitoring (MRM) assay, where precursor ions of proteotypic peptides for potential biomarker proteins are specified ab initio, as the second phase of a biomarker discovery platform [67]. A typical shotgun proteomics platform can perform deep, untargeted discovery of biomarker candidates and proteotypic peptides of interest can then be used to create an MRM assay which would consistently detect low-abundance peptides regardless of the intensity of co-eluting species. The selection of the best features for use in a MRM assay, as well as the selection of optimal biomarkers out of the many biomarker candidates generated by proteomics www.expert-reviews.com

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experiments, represents the interface of biomarker discovery platforms with engineering, computer science and mathematics. Aligned with aim 6, Ngo et al. [68] utilized precursor ion peaks as features in a genetic algorithm to construct a model that predicted attachment loss with 97% recognition capability and 67% cross-validation. Special care must be taken when selecting and applying algorithms for the stable selection of discriminating peptides or proteins in studies with low numbers of samples and high numbers of potential targets (i.e., high feature dimensionality in the biomarker feature selection problem, see [69] for review), as machine learning algorithms and statistical tests can overtrain on data artifacts. Baliban et al. [45] formulated a novel mixed-integer linear optimization model that selected the optimal subset of identified candidate protein biomarkers for the classification of CP from periodontally healthy subjects, while simultaneously constructing quantitative functions of diagnostic utility that resulted in 95% sensitivity and 100% specificity when applied to two blind test sets. Cross-validation studies randomized training set members and size and varied parameter values, and consistently reported cross-validation accuracy of over 99%. Together, these studies represent a paradigm shift as a post-processing step for the vast amount of data generated in shotgun proteomics biomarker discovery studies. Global analyses in proteomics which lead to biomarker subsets that survive clinical validation have utility in all disease studies, but are especially appropriate in a multi-faceted ‘complex’ disease such as periodontitis. Results from proteomics studies of periodontal disease

presents findings and common results supported by multiple studies that correlate with periodontal disease or periodontal health. In the sequel, we discuss the identified proteins that were up and downregulated in periodontitis and periodontal disease (i.e., periodontitis and gingivitis). MMPs, whose destructive role in periodontitis has been previously discussed, were reported as upregulated in both CP and GAgP in three studies [34,46,50]. A large amount of plasma proteins were associated with periodontal disease, such as hemoglobin [17,34,48,50,70], haptoglobin [34,39,48] and a-2-macroglobulin [46,71], the latter of which was only differentially detected in saliva. While studies that analyzed GCF discarded samples with apparent contamination by blood, the abundant presence of plasma proteins can be explained by increased tendency for spontaneous bleeding and the acutephase response to inflammation in periodontal disease, and has been investigated in CP [72]. A group of proteins related to actin, the major component of microfilaments, were found to be correlated with periodontal disease. Exposure to Treponema denticola, a Gramnegative bacterium associated with periodontal disease and a member of the ‘red complex’ group as defined by Socransky et al. [73], has been shown to induce actin rearrangement and gingival fibroblast detachment [74], while MMP inhibitors have been shown to reduce a-smooth muscle actin in model periodontal ligament cells [75]. TABLE 2

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Table 2. Consensus candidate biomarkers of periodontal disease from proteomics studies. Protein/protein class

Relevant biological function

Correlation

Medium

Ref.

Matrix metalloproteases

Collagen degradation

Upregulated in periodontitis†

GCF

Hemoglobin

Plasma protein

Upregulated in periodontal disease†

GCF, WUS

[17,34,48,50,70]

Haptoglobin and precursors

Plasma protein

Upregulated in periodontitis

GCF, WSS

[34,39,48]

Alpha-2-macroglobulin

Plasma protein

Upregulated in CP/UP

WUS

[46,71]

Actin/actin-related proteins

Microfilament formation


GCF, IPG

[15,50]

Profilin

Actin-binding


GCF, WSS

[34,46]

Plastins

Actin-binding


GCF

Adenyl cyclase-associated protein 1

Actin-binding


GCF, WSS

[46,50]

Apolipoproteins

Lipid-binding

A-1 downregulated in periodontal disease; B-100 upregulated in gingivitis

GCF, WUS

[44,47,70]

ATP-binding cassette proteins

Lipid homeostasis

Upregulated in periodontal disease

GCF, WUS

[44,47]

Alpha-amylase

Breakdown of polysaccharides


GCF, WUS

[47,49,71,84]

Carbonic anhydrase 1/2

Essential for bone homeostasis

Upregulated in CP

GCF, IPG

Catalase

Reduction of reactive oxygen species in inflammation


GCF, WSS

[44,46,50]

Cystatins

Protease inhibitors

Downregulated in periodontal disease

GCF, WUS

[34,50,70]

Fibrinogen

Inflammatory response


GCF, WSS

[46,50]

S100-A9

Inflammatory response, antimicrobial


GCF, WSS

[39,50]

Vitamin-D binding protein

Immune response


GCF, WUS

[48,49,84]

Neutrophil defensin 1

Antimicrobial


GCF, WSS

[44,46]

14-3-3 protein sigma

Multiple functions; can regulate epithelial cell growth

Downregulated in periodontitis

GCF, WUS, IPG

Carbonyl reductase 1

Catalytic enzyme

Downregulated in periodontitis

GCF

[34,46,50]

[34,46,50]

[15,44]

[15,48,50,84]

[48,50]

†

Periodontitis indicates correlation with CP and AgP; periodontal disease indicates correlation with both periodontitis and gingivitis. CP: Chronic periodontitis; GCF: Gingival crevicular fluid; IPG: Interproximal gingival tissues; UP: unclassified periodontitis; WSS: whole stimulated saliva; WUS: whole unstimulated saliva.

Increased levels of actins and actin-related proteins were detected in GCF and interproximal gingiva [15,50] of periodontitis patients. Also upregulated in periodontitis were the actin-binding proteins profilin [34,46] and plastin proteins [34,46,50]; an interesting note is that plastin-2 was reported as downregulated in gingivitis patients [47]. The actin-binding protein adenyl cyclase-associated protein 1 was upregulated in periodontal disease and has not been strongly associated with periodontal disease, though proteomics studies using THP1 cells have shown adenyl cyclase-associated protein 1 to be 36

upregulated by lipopolysaccharide structures of P. gingivalis [76], also a member of the ‘red complex’ and perhaps the most highly-implicated bacterium in CP. Fascinating correlations between lipid-regulators and periodontal disease were found by the proteomics studies. Links between periodontal disease and atherosclerotic disease have been extensively investigated partly due to epidemiological correlations and the overlap of many risk factors, including obesity, diabetes and smoking [77]. High-density lipoprotein (HDL), commonly associated with cardiovascular health, has been Expert Rev. Proteomics 11(1), (2014)



observed to be downregulated in periodontitis [78], and it has been suggested that periodontitis diminishes the antiatherogenic potency of HDL [79]. Proteomics studies found apolipoprotein A-1, the major component of HDL, to be downregulated with periodontal disease [44,70]. In contrast, low-density lipoprotein (LDL), commonly associated with cardiovascular disease, has been observed to be upregulated in periodontitis [78], supported here by the upregulation of apolipoprotein B-100, the major component of LDL, in gingivitis cases [47]. Additionally, the outer membrane vesicles of P. gingivalis have been observed to form aggregates with LDL, possibly stimulating foam cell formation [80]. Also relevant is the observation that ATP-binding cassette proteins, which are involved in lipid homeostasis [81], were detected with increased frequency in cases of periodontal disease [44,47]. Numerous observations reported in TABLE 2 overlap with findings in the literature about periodontal disease or have relevant functions. a-amylase, long investigated as a potential diagnostic salivary biomarker with varying conclusions [82,83], was consistently upregulated in periodontal disease [47,49,71,84]. Cytosolic carbonic anhydrases, which are enzymes in bicarbonate reactions, have been observed to be crucial in bone resorption in general as well as improper calcification in the chronic inflammatory disease ankylosing spondylitis [85,86]; they were observed to be upregulated in CP [15,44]. Another enzyme, catalase, has been detected at higher levels in plasma, red blood cells and gingival tissues in CP patients, which is a reaction to increased reactive oxygen species from inflammation [87]. Observations here agree, with upregulated levels observed in GCF and WSS [44,46,50]. With one dissenter [49], studies found that cystatin levels and cystatin precursor levels were downregulated in periodontal disease [34,50,70,71]. Cystatins are protease inhibitors that naturally have been investigated in periodontitis before; Gonçalves et al. [70] discuss the varying results in the literature, with some studies (including [47] reporting no significant difference in cystatin abundances. Fibrinogen is another acute-phase protein associated with inflammation and heartdisease that was upregulated in CP or unclassified periodontitis [46,50]. One study correlated increased fibrinogen levels with indicators of periodontal status [88], while another increased levels of fibrinogen in CP patients as well as increased incidence of genotypes associated with higher plasma fibrinogen levels [89]. The S100 proteins have been implicated in the inflammatory response; proteomics studies found S100-A9 levels increased in CP/UP [39,50]. Interestingly, S100-A9 has been shown to be involved in increased neutrophil stimulation and migration [90], and evidence suggests that P. gingivalis requires S100-A9 upregulation for smooth muscle cell proliferation in aortic hyperplasia [91]. Vitamin D binding protein was found to be upregulated in both forms of periodontitis by three studies [48,49,84] and downregulated in GAgP in one study [34]; a non-proteomics study found vitamin D binding protein levels to be significantly increased in GAgP patients, but noted that smoking www.expert-reviews.com

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regardless of periodontal status was also moderately correlated with the protein’s abundance. There are also a group of protein correlations which are not clearly explained by biological function or previous studies. The antimicrobial protein neutrophil defensin 1, or a-defensin 1, was correlated with periodontal disease [44,46] and found to be upregulated in the recovery phase of an experimental gingivitis model [17], but there are varying results in the literature on the correlation of the a-defensins to periodontal disease. Typically b-defensins are associated with periodontal immune response [92]; a-defensins 1-3 have been detected in saliva and at much lower concentrations in GCF and have been previously reported as highly upregulated in CP (60-fold) and AgP (15-fold), but display little antimicrobial activity against P. gingivalis and Aggregatibacter actinomycetemcomitans [93]. Patient variability may be a factor, as one proteomics study found a-defensins 1-3 to be upregulated in obese subjects versus non-obese subjects [94], another proteomics study did not detect a-defensins 1–3 in AgP patients [34] and other non-proteomics studies do not establish a consistent correlation between the abundances of the a-defensins and periodontal status [95,96]. Stratifin, otherwise known as 14-3-3 protein s, is involved in many signaling pathways, and was consistently upregulated in healthy patients [15,48,50,84]. Clinical implications of 14-3-3 protein s in a variety of settings remain muddled [97]. Finally, the catalytic enzyme carbonyl reductase 1 displayed increased levels in GCF in patients without periodontitis [48,50], though the clinical ramifications of this are not clear. Expert commentary & five-year view

The application of proteomics technologies and platforms toward the study of periodontal disease is still emerging. The studies reviewed here, primarily focused on profiling differences between disease and healthy state, and were discovery in nature. Their results are mostly applicable as indicators of disease onset and in the diagnosis of periodontitis. These results can yield hypotheses about the transition of gingivitis to periodontitis as well; this area would benefit from its own controlled study, as many of the biomarker candidates in TABLE 2 apply to both gingivitis and periodontitis. Temporal biomarkers for disease activity, progression and patient response are still not evident, and will likely be the focus of future proteomics studies. On the whole, the aforementioned areas where biomarkers would have the most clinical utility are still underexplored; new well-controlled proteomics studies should be initiated to elucidate possible pathogenesis triggers, mechanisms of active versus inactive sites and the temporal profile of a periodontal site through treatment and returning to homeostasis. The experimental design of these studies also has room for expansion. The specificity advantages of GCF over whole saliva have been discussed, but whole saliva has a role as a complementary or alternative medium early in the timeline (i.e., for disease onset prediction or when rapid collection is 37


Review


necessary) or in a Phase II study attempting to extend GCF biomarkers to saliva (well supported by overlap of GCF and salivary candidate biomarkers as shown in TABLE 2), and saliva does have unique roles in oral inflammatory responses. A proteomics study utilizing simultaneous collection of both fluids in healthy and diseased patients might reveal the parallels and orthogonalities of indicators of disease between two fluids. With one exception, studies did not stray from utilizing either GCF or whole saliva; there may be new diagnostic findings or confirmations with the proteomic study of subgingival plaque [98] or acquired enamel pellicle [99]; it may be relevant that many of the emphasized candidate biomarkers of TABLE 2 have also been detected by proteomics platforms in acquired enamel pellicle [100]. The different proteomics platforms discussed here can exist in a complementary mode as well. The classical 2DE experimental platforms remain the most robust way to determine multiple protein isoforms and provide protein-level analyses that can confirm and enhance the results of shotgun proteomics platforms. Online bottom-up platforms provide unbiased, sensitive and rapid information about the sample, but researchers must carefully select and apply advanced peptide and protein identification and quantification strategies that can handle the vast amounts of data generated. Future studies may utilize untargeted shotgun proteomics platforms as a discovery Phase I, which will lead to a funneled MRM assay for more stable detection and confirmation of each peptide of interest, as well as more accurate quantification in downregulated species. The application and development of advanced algorithms to selectively parse the wealth of data provided by

proteomics experiments for the most promising targets has only begun to be realized and should rise in prominence in clinical proteomics applications in general. Candidate biomarkers must be robust to subject heterogeneity to proceed through clinical validation; the majority of the studies cited here displayed limited sample sizes, such that subject variability in GCF or saliva composition may lead to biomarker candidates that would yield false positives at a larger scale. This variability, as well as a degree of experimental platform orthogonality, limits the applicability of proteomic cataloguing of healthy saliva and GCF profiles to periodontal disease, though the identification of areas of high and low variability is of utility [35]. Proteomics platforms for the study of periodontal disease and the discovery of biomarkers require careful consideration of experimental design and their inherent limitations and difficulties, but they remain powerful and appropriate in clinical settings, especially for complex multi-faceted diseases such as periodontitis. Financial & competing interests disclosure

CA Floudas acknowledges financial support from the National Science Foundation (CBET-0941143) and the National Institute of Health (R01LM009338). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

Key issues • Periodontitis is a common, chronic and destructive disease that affects individual as well as public health costs. The pathogenetic mechanisms of periodontitis are not well understood. • The correlation of periodontitis as a risk factor for various systemic conditions has been extensively investigated, rendering the search for biomarkers of periodontitis extremely useful. Protein biomarkers can be applied in practice to many phases of periodontitis, from disease onset to clinical endpoint. • The literature contains a limited number of differential proteomics studies on periodontitis. These studies have outlined a number of candidate protein biomarkers correlated with periodontal disease or health, summarized in TABLE 2, including actin-binding proteins, plasma proteins, lipoprotein components, antimicrobial peptides and catalytic enzymes. • Gingival crevicular fluid as a sample medium is highly disease-specific, but its collection remains technically demanding. Collection of whole saliva is rapid and nontechnical, but analyses are complicated by changes in composition, glandular contributions and protein abundance ranges. • Utilization of two-dimensional electrophoresis as the driver of proteomics studies remains the primary choice for straightforward quantification and identification of protein isoforms. High-throughput bottom-up proteomics platforms greatly increase the number of identifications made and have risen in prominence, but quantification requires more advanced methods and the vast amounts of data produced must be processed robustly. • Advanced algorithms for the selection of small biomarker subsets from vast datasets have been applied to proteomics studies of periodontitis with success, and will continue to be developed. • Certain areas that would benefit from the discovery and application of biomarkers, such as prediction or determination of disease onset or temporal disease stage indicators, remain unexplored by large-scale proteomics studies. Pooled and site-specific analyses can greatly contribute to the investigation of dynamic changes of the proteome after treatment and in responding versus non-responding sites.

38

Expert Rev. Proteomics 11(1), (2014)


tetracyclines. Pharmacol. Res. 63(2), 108–113 (2011).

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