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Clinical Biochemistry 46 (2013) 506–517

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One decade of salivary proteomics: Current approaches and outstanding challenges Francisco M.L. Amado a, b,⁎, Rita P. Ferreira a, Rui Vitorino a a b

QOPNA, Mass Spectrometry Center, Department of Chemistry, University of Aveiro, Aveiro, Portugal School of Health Sciences, University of Aveiro, Aveiro, Portugal

a r t i c l e

i n f o

Available online 25 October 2012 Keywords: Gel-based Gel-free Electrophoresis LC-MS/MS Peptidomics PTMs

a b s t r a c t Efforts have been made in the last decade towards the complete characterization of saliva proteome using gel-based and gel-free approaches. The combination of these strategies resulted in the increment of the dynamic range of saliva proteome, which yield in the identification of more than 3,000 different protein species. Comparative protein profiling using isotope labeling and label free approaches has been used for the identification of novel biomarkers for oral and related diseases. Although progresses have been made in saliva proteome characterization, the comparative profiling in different pathophysiological conditions is still at the beginning if compared to other bodily fluids. The potential biomarkers identified so far lack specificity once common differentially expressed proteins were detected in the saliva of patients with distinct diseases. In addition, recent research works focused on saliva peptidome profiling already allowed a better understanding of peptides’ physiological role in oral cavity. This review provides an overview of the major achievements in saliva proteomics giving emphasis to methodological concerns related with saliva collection, treatment and analysis, as well as the main advantages and pitfalls underlying salivary proteomic strategies and potential clinical outcomes. © 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Introduction One decade after the introduction of proteomics and following the advances in mass-spectrometry, a tremendous progress in disclosing the complete salivary proteome was noticed, demonstrated in more than 3000 different proteins already identified [1–7]. Proteomics must not only be seen as a systematic separation and cataloguing approach to study all the proteins expressed in an organism, but it must be also seen as a tool for a better understanding of how protein structure change and interact with other proteins, and ultimately how it reflects disease or health in an organism. Thus, qualitative and quantitative analysis of saliva proteome is relevant not only for basic research but also for high-throughput analysis of saliva as a fluid with diagnostic and prognostic value [8–10]. This research field is continuously growing, as noticed by the number of studies retrieved from Scopus using saliva and proteomics as keywords, which accounts in more than 300 papers. The present review provides a brief overview of the major achievements

Abbreviations: C4, Reverse Phase C4; DIGE, Differential Electrophoresis; ESI, Electrospray; IEF, Isoelectric focusing; IMAC, Immobilized Metal Affinity Chromatography; iTRAQ, Isobaric tag for relative and absolute quantitation; LC, Liquid Chromatography reverse C18; MALDI, Matrix Assisted Laser Desorption Ionization; MS/MS, Tandem mass spectrometry; P, Parotid saliva; SCX, Strong cation Exchange; SDS, Sodium dodecylsulphate; SELDI, Surface-enhanced laser desorption/ionization; SL, Sublingual saliva; SM, Submandibular saliva; SWS, Stimulated whole saliva; WB, Western blot; WCX, Weak cation exchange; WS, Whole saliva; 2DE, Two-dimensional gel electrophoresis. ⁎ Corresponding author at: Department of Chemistry, University of Aveiro, 3810–193 Aveiro, Portugal. Fax: +351 234370084. E-mail address: [email protected] (F.M.L. Amado).

obtained with proteomics tools as well as the drawbacks underlying saliva collection, pre-treatment and analysis. Preanalytical factors to be considered in proteome/peptidome analysis Interest in saliva as diagnostic fluid has attracted many researchers in last decades leading to the development of diagnostic tools based on saliva analysis to monitor, among others, hereditary diseases [11,12], autoimmune diseases [13,14], malignancies [15–18], oral diseases such as dental caries [19–21] or periodontitis [22–24]. Saliva is derived from the secretions of the three-paired major salivary glands, the parotid, submandibular and sublingual glands, numerous minor salivary glands spread over oral cavity, and gingival crevicular fluid [25,26]. This bodily fluid offers an alternative to blood, plasma, serum or urine for diagnostic purposes mainly due to (i) its non-invasive nature, painless and costeffective collection; (ii) can be used to study special populations where blood sampling is a problem as in case of children; (iii) saliva composition presents less salt concentration when compared to urine; (iv) the total protein concentration does not reflect the contribution of a small set of highly abundant proteins like in the case of blood-derived products (e.g. albumin and globulins) [10,27]. Saliva obtained by passive drool, a widely used procedure of sample collection, may present high viscosity which difficult its handling in laboratory. To overcome this drawback, saliva collection devices such as Salivettes [28,29] became very popular, since they are easy to handle and after centrifugation, the resulting saliva presents a lower contribution of mucins, being consequently less viscous allowing a better sample

0009-9120/$ – see front matter © 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.clinbiochem.2012.10.024

F.M.L. Amado et al. / Clinical Biochemistry 46 (2013) 506–517

processing. Recently, Topkas et al. [30] evaluated the effect of different saliva collection devices on the composition of this fluid by immunoassay of C-reactive protein, myoglobin and IgE and detected significant differences in analyte levels based on the collection method and device's material type. In addition, significant differences in the salivary flow rates were also observed depending on the saliva collection method. Although the most appropriate saliva sample collection method, according to our experience, is passive drool, in special cases such in case of xerostemia, collection devices are needed for saliva collection aiming proteome analysis [31]. Besides issues concerning saliva collections, variables such as gender [32–35], age [32–35], diet [34,36,37], circadian rhythm [38], interindividual variability [37,39–41] and sample stability [40,42–44] might influence data retrieved from proteomic analysis. Several works performed in the last decade on saliva analysis have been addressed to standardize saliva collection and processing. Since saliva contains microorganisms and proteases which may impact sample stability/protein degradation, careful control of temperature during saliva collection and sample storage is crucial and it is nowadays recognized that saliva collection should be performed on ice with the addition of protease inhibitor cocktail [43]. Xiao and Wong [40] also proposed the addition of ethanol to stabilize the salivary proteome without significant degradation at room temperature, for a maximum period of 2 weeks. Nevertheless, researchers must be aware that higher levels of salivary peptidesderived fragments can be produced with increased sample freezing rate independently of donor nutritional status as observed by de Jong et al. [45]. Furthermore, nutritional status as well as circadian rhythm influences protein expression as observed by Quintana et al. [39]. Thus, it is recommended that saliva samples should always be collected at the same time of the day to reduce the effect of circadian rhythm, and at least 2 h after eating, with a previous mouth wash. Whole saliva is no longer seen as a mixture of glandular secretions, with serum filtrate components, bacteria and desquamated cells from the oral epithelium contributing to its composition though in a relative proportion depending upon the type and degree of stimulation and even the time of the day [38,46,47]. Different approaches of whole saliva collection, with or without stimulation, have been adopted to study saliva composition, and the contribution of different glandular secretions to the whole proteome [1,43,48–50]. The majority of studies on saliva proteome characterization start with a clearance step to remove large aggregates (food and cell debris), producing a clear extract for subsequent analysis. This clearance procedure is particularly relevant when unstimulated saliva is analyzed. Stimulated saliva obtained with wax paraffin [51] can be collected easier and in higher volumes; however, this fluid is predominantly derived from the parotid gland and consists mainly of water whereas the major components of unstimulated saliva are mainly secreted from the submandibular gland and comprises mucins and cystatins, which largely contributes to the aggregation of salivary components [30]. These aggregates lead to the loss of salivary proteins/ peptides, which seem to be pronounced if the clearance step is performed after freezing/thawing cycles. Recently, our group proposed a procedure that aims the standardization of saliva treatment and the minimization of salivary components’ loss during the clearance step [52]. Besides the addition of anti-protease cocktail, which is mandatory in face of the high proteolytic activity existent in saliva, the proposed sample procedure involves the addition of a chaotropic/detergent solution with one sonication cycle. With this, several salivary proteins such as amylase, mucins, cystatins and histatin are recovered from the “waste” pellet resultant from the traditional saliva clearance step [53]. Albeit bacteria are always referred as part of saliva, the number of bacterial proteins identified in saliva is scarce being only possible when multidimensional approaches are used in saliva fractionation [52]. Rudney et al. [54] using a metaproteomic approach combining a three-dimensional strategy identified 139 proteins from 34 different microbial species. The analysis of these specific proteins might take particular importance for diagnostic purposes in oral diseases

507

where bacteria have a strong influence, as in the case of periodontitis [22,24,55]. Along with the discharge of bacteria in the saliva clearance step, squamous epithelial cells are also lost, which might impact biomarker screening. Indeed, Xie et al. [56] demonstrated the usefulness of these cells in the proteomics characterization of whole saliva from patients with diagnosed oral squamous cell carcinoma (OSCC) lesions, with numerous identified proteins belonging to OSCC signaling and tumorigenesis pathways. Methodological strategies for salivary proteome and peptidome profiling Nowadays, proteomics cannot be seen as a tool but as combination of conceptual innovations and technical advances in separation techniques, mass spectrometry and bioinformatics for data analysis and integration (Fig. 1). Within this concept, the monitoring of changes in the protein levels in response to different genotypes or pathophysiological conditions can provide a better understanding of complex biological processes and may allow inference of unknown protein functions as well as the discovery of potential clinical biomarkers [57]. Currently, proteome characterization can be performed using different tools classified in top-down and bottom-up proteomics. In top-down proteomics intact proteins are analyzed without proteolytic digestion, being subjected to gas-phase separation, fragmentation, fragment separation, and automated interpretation of mass spectrometric data, yielding both the molecular weight (MW) of the intact protein and the protein fragmentation ladders [58–60]. In contrast, bottom-up proteomics usually involves the analysis of peptides (resulting from enzymatic or chemical cleavage) with little or no emphasis on protein level resolving methods. In this case, chromatographic and electrophoretic strategies are always required to decrease the complexity of peptide mixtures before their analysis by mass spectrometry [61]. Both strategies have been widely applied to the characterization of salivary proteome (Fig. 2), which have largely contributed for demystifying their complexity. Gel-based approaches for salivary proteome characterization Before the burden of proteomics, the most abundant salivary proteins such carbonic anhydrase [62], mucins [63,64], proline-rich proteins (PRPs) [63,65], statherin [66], histatins [67], cystatins [63,68] and amylase [69] were characterized using the conventional gel electrophoresis approaches [70]. Initial studies on salivary proteome relied on the application of two-dimensional gel electrophoresis (2-DE) [71,72] for protein separation and quantitation (Fig. 2) followed by mass spectrometry (MS) for identification (Table 1). Salivary proteome analysis

Analysis

Proteomics Abundant protein depletion

Fractionation

Fig. 1. Integrative outlook of current methodological approaches for proteomic analysis highlighting the relation between protein/peptide separation, analysis and data treatment with bioinformatics tools.

508

F.M.L. Amado et al. / Clinical Biochemistry 46 (2013) 506–517

Saliva

Structural characterization and Quantification

Proteome

Peptidome

Gel-based

Gel-free

SDS-PAGE 2DE GeLC Zymography

SCX-RP RP-RP FFE-SCX-RP CE-RP nanobeads@Lectin IMAC/TiO2

DIGE

Label-free iTRAQ O

Gel-free

RP

Label-free iTRAQ O

Fig. 2. Flowchart of the current strategies used for saliva analysis aiming proteome/peptidome characterization.

performed by our group using 2-DE yielded on the identification of 39 non-redundant proteins from 90 gel spots (Table 1) [46]. Later on, other groups have intensively pursued the characterization of glandular saliva [49,73] and whole saliva [4,49,74] using the same approach and identified around 200 different proteins (Table 1). Despite the excellent sensitivity of MS, only the most abundant proteins can be analyzed with 2-DE, being many low-abundance proteins excluded as in the case of cytokines, which are typically present at attmoles ranges (Fig. 3). Furthermore, extremely acidic or basic, low and molecular weight proteins such as histatins, PRPs (basic and acidic) or mucins are outside the 2-DE range, representing a limitation for whole saliva analysis. In addition, the high content of glycoproteins in saliva might compromise the focusing separation reflected in the protein spot position on 2-DE gels, with consequences for its reproducibility [75–77]. Nevertheless, this approach presents advantages in the characterization of saliva proteome post-translational modifications (PTMs) as well as in the distinction between intact proteins and their fragments [77]. For example, Hirtz et al.

[78] showed that salivary amylase present a stable but very complex pattern with its identification in more than 100 spots. Along with truncated forms, glycosylated forms of salivary amylase corresponding to different N-glycosylation sites might be found in different gel spots (Table 1) [79]. This suggestion was further confirmed by Ramachandran et al. [80] using a methodological approach relied on salivary proteins treatment with the enzyme PNGase F before 2-DE separation. At the end, these authors identified several N-glycosylated sites from 16 different salivary glycoproteins including amylase. Later, Sondej et al. [81] combined 2-DE, lectin blotting and identification by mass spectrometry to map the salivary glycome. Using Maackia amurensis II lectin (Mal II) 128 out of 166 2-DE gel spots present reactivity suggesting that most of the salivary proteins contain a (α-2,3)-linked sialic acid residue. Similarly to glycosylation, protein phosphorylation promotes alterations in the apparent molecular weight and isoelectric point (pI) of a protein representing an increment on the 2-DE spot number as observed for cystatin S usually detected in 3 spots corresponding to 0, 1 and 2 phosphorylation sites [46].

Table 1 Summary of gel-based methodologies used for salivary proteome. Methodology

Goal

Sample

Results

Ref.

2-DE-MALDI-TOF-MS/Q-TOF-MS 2-DE-MALDI-TOF-MS 2-DE-MALDI-TOF/TOF

Characterization of salivary proteome

WS

8 proteins identified 20 proteins identified 39 proteins identified 21 proteins identified 140 spots identified as α-amylase 64 proteins identified 34 proteins identified 218 proteins identified 318 proteins identified

[167] [71] [46] [73] [78] [4] [49] [42] [1,7]

6 proteins identified 45 N-glycoproteins identified 128 out of 166 2-DE gel spots present reactivity suggesting that most of the salivary proteins contain a (α-2,3)-linked sialic acid residue

[154] [80] [81]

SDS-PAGE-LC-MS/MS 2-DE-LC-MS/MS SDS-PAGE-WB SDS-PAGE/2-DE-Pro-Q Emerald-LC-MS/MS 2-DE-Lectin

Proteome comparison between whole saliva and plasma Characterization of salivary micelles Characterization of salivary glycoproteome

P SWS WS WS, P; SM/SL WS WS SWS WS

F.M.L. Amado et al. / Clinical Biochemistry 46 (2013) 506–517

509

Gel-free-Based proteomics /ELISA

Gel-Based proteomics

Normal Range Abundances Log 10 concentration in pg/ml

0

2

4

6

8

10

12

14

Fig. 3. Overall analysis of the relative abundance of 50 distinct proteins already identified in the saliva from healthy individuals. Abundance is plotted on a log scale spanning 14 orders of magnitude. Data were obtained from published works [35,139,141–166], based on immunoassays. Some classical plasma proteins were also introduced in order to emphasize their presence in significant levels in saliva.

One-dimensional SDS-PAGE, termed GeLC-MS/MS (Fig. 2), has been recently used for protein fractionation, as a result of its simplicity and reproducibility [82]. Using this strategy, proteins are typically in-gel digested and the resulting peptides are fractionated in a reversedphase-HPLC coupled to a mass spectrometer for protein identification. As a result, substances (e.g. salts) [83] or protein complexes [84] which interfere with downstream mass spectrometric acquisition are eliminated/disrupted resulting in the identification of more than 200 proteins (Table 1) [42]. However, this approach does not allow the identification of the abundant low molecular weight salivary proteins due to their small size or to the non-existence of cleavage sites for trypsin digestion. Nevertheless, the utilization of other enzymes such as chymotrypsin [85], protease V-8 [85] or proteinase K [86] may contribute to a better effective protein digestion. By using the Bis–Tris system, a better resolution of low molecular proteins is achieved concomitantly with the separation of high molecular proteins [53,87,88]. Overall, this could be a promising strategy since the dynamic range of protein abundances is enlarged and label-free quantitation can be performed after band digestion followed by LC-MS analysis [89]. Gel-free based approaches for salivary proteome characterization Gel-free approaches are an attractive alternative to 2-DE since allows the analysis of salivary proteome in a wider dynamic range and broader proteome coverage (Fig. 3). In fact, in the last decade, these strategies have provided complementary valuable information to 2-DE since allow to overcome its disadvantages such as the laborious procedure involved, the large amount of sample required, the limited dynamic range, the difficulties in resolving low abundant proteins and the ones with extreme pI, molecular weights and hydrophobicity (e.g. membrane proteins) [90]. In shotgun proteomics, a mixture of proteins is digested into peptides that are loaded onto at least a two-dimensional chromatography based separation system. Peptides are then eluted into a tandem mass spectrometer, in an automated fashion, and the resulting tandem mass spectrometry data is analyzed by powerful computational systems [91]. Large scale shotgun proteomics was initially defined as an ion exchange chromatography (specifically strong cation exchange-SCX) coupled to reverse phase (RP) and mass spectrometry

(Fig. 2) [92]. Using SCX-RP-MS/MS (Table 2) Wilmarth et al. [2] identified 102 non-redundant proteins including the most known salivary proteins and a large set of serum proteins. A different gel-free strategy was adopted by Hu et al. [4] and consisted in the saliva fractionation by ultrafiltration in three fractions according to the molecular weight b3, b10 and >10 kDa, followed by the digestion of the last two fractions and their analysis by LC-MS/MS. At the end, a total of 266 nonredundant proteins were identified (Table 2). Nowadays, alternative configurations to SCX with RP including the use of anion exchange chromatography and RP, affinity chromatography (AC) and RP, isoelectric focusing (IEF) and RP, and capillary electrophoresis (CE) were evaluated (Fig. 2) [74,93]. Indeed, Xie et al. [5] using preparative IEF by free flow electrophoresis (FFE) identified 437 non-redundant proteins. Recently, a RP-RP system was proposed with a first RP separation at pH 10 and the second RP at pH 2.6 [61,94]. This later approach yielded higher proteome coverage when compared with the SCX-RP approach [61,95]. Adopting this combination, 489 non-redundant proteins were identified, being 440 assigned to human and 49 to lactobacilli [53]. Overall, the use of different combinations by different research groups yielded in the identification of more than 3000 non-redundant proteins in saliva, including microbiota proteins (Table 2) [1,6,7,27,54]. The analysis of global proteome, in which hundreds or thousands of proteins can be present, represents always a challenge, especially if PTMs analysis is included [96]. As previously referred, PTMs overview is easily obtained using gel-based approaches with specific stains. However, the knowledge of the exact modification and in which amino acid residues occur is crucial to the understanding of proteins’ physiological roles as well as the molecular pathways in which they participate. Thus, gel-free based approaches involving chemical or protein affinity for the capture of modified proteins have been widely used in the characterization of the most abundant PTMs (glycosylation and phosphorylation) in saliva (Table 2). For instance, Ramachandran et al. [80] performed a large scale enrichment of glycoproteins through carbohydrates oxidation of glycoproteins which are then retained on a hydrazide resin. The N-linked glycopeptides are then released using the enzyme PNGase F and the modified peptides can be identified by mass spectrometry. This approach resulted in the identification of 84 N-glycosylated peptides from 45 unique N-glycoproteins. The same research group

510

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Table 2 Summary of gel-free methodologies used for salivary proteome and peptidome characterization. Methodology

Goal

Sample

Results

Ref.

SCX-LC-MS/MS Filter-LC-MS/MS Free flow electrophoresis-LC-MS/MS Capillary IEF-LC-MS/MS Filter/SCX-LC-MS/MS

Characterization of salivary proteome

WS

102 proteins identified 269 proteins identified 437 proteins identified 1381 proteins identified 1166 proteins identified: 914 in P; 917 in SM/SL 2340 proteins identified

[2] [81] [5] [74] [1]

10 proteins identified 449 proteins identified 56 proteins identified

[48] [7] [54]

1222 proteins identified

[101]

139 proteins from microbiota identified

[97]

SM/SL

262 N- and O-linked glycoproteins identified

[100]

WS, P, SM/SL

156 N-glycosylated peptides representing 77 unique N-glycoproteins: 62 (WS); 34 (P), 44 (SM/SL) 68 glycoproteins identified: 48 ConA, 39 WGA, 37 MA

[98]

P, SM/SL

Proteominer beads –Preparative isoelectric focusing (IEF)-SCX-LC-MS/MS IEC-LC-LC-MS/MS LC-LC-MS/MS SCX-LC-MS/MS Transient capillary isotachophoresis/CZE (CITP/CZE) Free-flow electrophoresis system (FFE)-SCX-LC-MS/MS Agarose-lectin beads (covalently linked to Con A, RCA-I and UEA-I)-SCX-LC-MS/MS Hydrazide-LC-MS/MS

Magnetic-beads (covalently linked to Con A, WGA and MA)-LC-MS/MS Proteominer beads –SCX-LC-MS/MS IMAC (TiO2)-LC-MS/MS Proteominer beads –SCX-IMAC(PHOS-Select affinity gel)-LC-MS/MS Chemical derivatization-Sepharose 4B glutathione–2-pyridyl disulfide-LC-MS/MS IMAC (Fe)-LC-MS/MS LC-MS/MS

WS

Proteome comparison between whole saliva and plasma Characterization of salivary Microbiota Characterization of salivary glycoproteome

Minor Salivary Gland saliva WS

WS

Characterization of salivary phosphoproteome

Characterization of salivary peptidome

WS

Whole saliva, Parotid, Submandibular

performed a comparative analysis between whole saliva, parotid fluid, submandibular fluid, and sublingual fluid and expanded to 77 the number of unique N-glycoproteins [97]. Through the application of this strategy in combination with dynamic range compression (DRC) method, Bandhakavi et al. [98] identified 193 glycoproteins. Another efficient strategy for glycoprotein enrichment takes advantage of the lectins capability to selectively bind complex glycans and discriminate between subtly different structural motifs. For instance, concanavalin A (ConA) is regarded as a broad spectrum lectin having affinity for mannosyl and glucosyl residues in glycans whereas wheat germ agglutinin (WGA) binds GlcNAc residues [99]. Lectins have been traditionally immobilized on solid supports (such as silica, agarose, or sepharose) which facilitated both protein recovery and increased sensitivity [99]. Taking in consideration the analytical potential of magnetic nanoparticles (MNPs), our group developed and optimized a platform for the selective enrichment of glycoproteins based on lectin-functionalized MNPs (MNP@lectins). Combining multiple lectins including ConA, WGA and Maackia amurensis (MA), selective recovery of glycoproteins was obtained with the identification of more than 68 different glycoproteins, 48 of which were recovered with ConA, 39 with WGA, and 37 with MA [100]. By using more lectins, targeting distinct glycan motifs, it is expected to increase the number of identified glycoproteins. Using affinity columns with three distinct immobilized lectins, Gonzalez-Begne et al. [101] identified a total of 262 N- and O-linked glycoproteins. Taking benefit from sialic acid that is typical of terminal monosaccharide from cell surface glycoconjugates, Larsen et al. [102] developed an elegant method to isolate sialic acid-containing glycoproteins in whole saliva. This method is

[6]

[102]

193 glycoproteins identified 45 glycoproteins idenfied 217 unique phosphopeptides sites identified representing 85 distinct phosphoproteins 65 phosphoproteins identified

[105] [104] [41,73,86,108,111,112, 114,116,117,155–157]

10 phosphoproteins identified Several peptide fragments and PTMs were identified

[81] [5]

[2]

based on titanium dioxide (TiO2) commonly used to purify and measure phosphoproteins/phosphopeptides. These authors used a low-pH buffer containing glycolic acid to promote binding efficiency and selectivity for acidic groups and at the end they identified 97 unique formerly sialylated glycopeptides from 45 sialylated glycoproteins (Table 2). Besides glycosylation, efforts have been done in the development of enrichment systems devoted to the analysis of phosphorylated proteins. Most of these methods are based in chemical affinity. For instance, using immobilized metal affinity chromatography (IMAC), a widely used enrichment technique for phosphorylated peptides where metal ions bind negatively charged phosphopeptides, Cirulli et al. [103] identified 139 phosphopeptides from 10 salivary proteins. Salih et al. [104] used disulfide–thiol interchange covalent chromatography of derivatized phosphoserine and phosphothreonine containing proteins under base-catalyzed conditions by thiol agents and identified 65 phosphoproteins. Recently, Stone et al. [105] applied a similar strategy based in DRC and IMAC and identified a total of 217 unique phosphopeptides pertaining to 85 distinct proteins. Gel-free based approaches for salivary peptidome characterization Similarly to other bodily fluids, saliva contains several protein species of low molecular weight, most of which are likely proteolytic fragments of larger proteins, comprising around 20–30% of the total secreted proteins [106], which largely contributes to the oral cavity homeostasis (reviewed in [47,79,107]). Salivary peptidome profiling may contribute to a better understanding of proteins’ biological role, which

F.M.L. Amado et al. / Clinical Biochemistry 46 (2013) 506–517

might be compromised in disease conditions. With this in mind, salivary peptidome has been explored and so far more than 2000 different species were identified [41,44,107–112]. Efforts in this sense have yielded in the characterization of new species such as cystatin B as S-modified derivatives [113]. Using top-down approaches, two new variants of peptide P-C were reported and suggested to result from nucleotide polymorphism [60]. Saliva is an atypical bodily fluid when compared to urine, serum, plasma or cerebrospinal due to the fact that 20–30% of all identified peptides belongs to the major salivary peptide classes (including statherin, PRPs, histatins, SMR3B (P-B peptide)). Technically, saliva peptidome analysis is straightforward since, with the exception of ammonium sulfate, all traditional extraction procedures (organic solvent, chaotropic

511

agents, acid or ultrafiltatrion (UF)) result in peptide-rich extracts [52]. However, most of the works focused in peptidome characterization used trifluoroacetic acid [86,114–118] or ultrafiltration to extract peptides [45,46,73,111,119]. Recently, acetonitrile combined with ammonium hydrogenocarbonate and UF was proposed based on the higher yield of extracted peptides [52]. Due to gel-based limitations regarding the separation of low molecular weight species (b5 kDa), peptidome analysis mainly relies in gel-free approaches (Fig. 2). Thus, after peptide extraction, their analysis is performed by LC-MS/MS. The resultant MS/MS spectra can be processed in automated fashion using non-redundant databases (defining no enzyme) where hundreds of peptide sequences are assigned or can be inspected manually using de novo sequencing tools

Table 3 Summary of the identified protein markers for each oral and systemic disease. Approach

Pathophysiological condition

Methodology

Molecular findings

Ref

Gel-based

Chronic periodontitis patients

2DE-MALDI-MS/MS and LC-ESI-MS/MS 2DE-MALDI-MS/MS and LC-ESI-MS/MS SDS-PAGE-MALDI-MS/MS

Increased: albumin; hemoglobin; immunoglobulin proteolysis decreased: cystatin Increased: albumin; amylase

[24]

Increased: annexin A1; beta- and gamma-actin; cytokeratin 4 and 13 zinc finger proteins and P53 Increased: carbonic anhydrase VI Increased: GRP78/BiP Increased: transferrin Increased: S00A8; S00A9; S00A6 Increased: albumin; Ig gamma2 chain C region; Ig alpha2 chain C region; vitamin D-binding protein, amylase; zinc-alpha2 glycoprotein Decreased: lactotransferrin; elongation factor 2; 14-3-3 sigma, short palate, lung and nasal epithelium carcinoma-associated protein 2 precursor; carbonic anhydrase 6 Increased: M2BP; profilin; CD59; MRP14; catalase; histone H1; S100A12; Ras-related protein Rab-7; moesin; involucrin Decreased: statherin; truncated cystatin S Increased: beta fibrin; S100; transferrin; Ig heavy chain constant region gamma; cofilin-1 Decreased: transthyretin Increased: amylase; enolase; actin; carbonic anhydrase I and II Decreased: cystatin (S, SA, C, SN, D); carbonic anhydrase VI Increased: alpha-1-B-glycoprotein; complement factor B Decreased: cystatin S; parotid secretory protein; Poly-4-hydrolase beta-subunit Increased: amylase; Ig A; lactoferrin Decreased: lipocalin; cystatins S; cystatin SN Increased: annexin A1; HBA2, CST5, cystatin D, cystatin S, zinc alpha2-glycoprotein, human calprotectin Decreased: carbonic anhydrase VI; lipocalin; S100A9, S100A8 Increased: STAT3; RIPK2; IKBK Increased: α-2-microglobulin; lactotransferrin; amylase Increased: glyceraldehyde-3-phosphate dehydrogenase; Serum amyloid A Increased: m/z 1817.7 Da, m/z 2710 Da, m/z 2744.8 Da; m/z 4134 Da Increased: actin; myosin

[154]

Increased: truncated cystatin SA-I Increased: m/z 1472.78 Da; m/z 2936.49 Da, m/z 6556.81 Da; m/z 7081.17 Da Increased: alpha-2 macroglobulin; alpha-1-antitrypsin; cystatin C; transthyretin amylase Forty-nine proteins differentially expressed Increased: albumin; α and β haemoglobin chains; α-defensins 1, 2 and 3 Decreased: α-defensins in periodontitis obese patients Increased: m/z 3738 Da; m/z 11366 Da

[134] [164]

Gingivitis Head and neck squamous cell carcinoma Non-invasive breast cancer Rheumatoid arthritis Oral squamous cell carcinoma Periodontitis Aggressive periodontitis

Gel-based

Gel-free-based

2DE (DIGE)-MALDI-MS/MS 2DE-MALDI-MS/MS 2DE-MALDI-MS/MS 2DE-MALDI-MS/MS 2DE-MALDI-MS/MS

Oral squamous cell carcinoma

C4, 2DE-MALDI-MS/MS

Dental caries Head and neck squamous cell carcinoma

IEF-SDS-PAGE-MALDI-TOF/TOF 2DE (DIGE)-LC-MS/MS

Sjögren's syndrome

2DE-LC-ESI-MS/MS

Head and neck squamous cell carcinoma

SDS-PAGE-LC-MS/MS

Dental caries

2DE-MALDI-MS/MS

Lung cancer

2DE-(DIGE)-MALDI-MS/MS

Oral squamous cell carcinoma Sjögren's syndrome Type 2 diabetes in elderly

FFE/SCX-LC-ESI-MS/MS; iTRAQ SCX-LC-ESI-MS/MS; label free LC-ESI-MS/MS

Orthodontic treatment

WCX-MALDI-TOF

Pre-malignant and malignant oral lesions Oral squamous cell carcinoma Gastric cancer

SCX-LC-ESI-MS/MS; iTRAQ

Type 2 diabetes

SCX-LC-ESI-MS/MS; label-free

Breast cancer Periodontitis with obesity

SCX-LC-ESI-MS/MS; iTRAQ SELDI

Oral squamous cell carcinoma and oral leukoplakia Type 1 diabetes

SELDI

Dental caries Type 1 diabetes Type 1 diabetes and oral oral squamous cell carcinoma

LC-ESI-MS/MS LC-MALDI-MS/MS LC-MALDI-MS/MS

SELDI WAX-MALDI-TOF/TOF

LC-ESI-MS/MS

Increased: α-defensins 1, 2 and 4; S100A9 Decreased: statherin SMR3B (P-B peptide); histatins 1 e 5 Decreased: aPRP; histatin 1; Statherin Increased: proteolysis Increased: HexNac modification in bPRPs

[167]

[155] [156] [157] [23] [121]

[158] [159] [18]

[160] [161]

[21] [125]

[56] [162] [138] [163]

[165] [137] [136]

[166] [167] [12] [168] [11]

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in order to identify new sequences or PTMs [41,108,112]. Indeed, tyrosine sulfation, a widespread PTM implicated in the intracellular trafficking of secreted peptides, could be mismatched with phosphorylation since presents the same neutral loss of 80 Da. The manual spectra interpretation resulted in the identification of four sulfated tyrosines (Tyr 27, 30, 34, and 36) in histatin 1 [86]. Another example was the identification of cyclo-statherin at Gln-37, where a cyclic derivative is transformed by the action of transglutaminase 2 [115]. Behind the typical phosphorylation detected in multiple peptides from histatin 1, statherin and PRPs, the presence of N- and O-glycosylation sites was also detected in basic PRPs [120]. Strategies for biomarker discovery in saliva and clinical outcomes During the last decade, several studies have been performed taking in consideration the advantages of the above-referred methodological approaches coupled with quantitative strategies aiming to identify protein targets in saliva for disease diagnosis. A global quantitative analysis of human salivary proteins without resource to a mass spectrometer can be easily obtained using 2-DE strategy, being widely used for biomarker discovery, namely of oral diseases, dental caries [21] and periodontitis [121], and also of other pathophysiological conditions such as Sjögren syndrome (SS) and non-Hodgkin's lymphoma [122]. In addition

to biological variability, technical bias induced by protein migration during the focusing step, or by gel staining, might difficult spot detection and their boundaries, making 2-DE gel analysis a hard task. Thus, reliable and confident data require several replicates per sample to achieve a coefficient variation in the range of 20–30% [75]. To overcome these difficulties, 2-D DIGE was introduced, which is based in the use of two or three mass- and charge-matched N-hydroxy succinimidyl ester derivatives of the fluorescent cyanine dyes Cy2, Cy3, and Cy5, with distinct excitation and emission spectra. Each labeled sample is then mixed and run simultaneously on a single 2-DE gel [123]. Within this, two or three samples can be separated under identical electrophoretic conditions, reducing the number of gels required while allowing moreaccurate comparative proteome profiling. This strategy has been applied to saliva in the evaluation of protein profiles among breast cancer patients [124] and in lung cancer [125], being carbonic anhydrase VI found in higher and lower levels, respectively (Table 3). Over the last years we have assisted to the development of many different quantification methodologies for gel-free based proteomic analysis, all related to each other once they exploit the signal from the mass spectrometer for quantification at the peptide level [126,127]. The most common strategies are based in the introduction of stable isotopes in peptides and proteins through metabolic labeling in vivo (SILAC, SILAM), chemical reactions (i.e. ICAT, TMT, mTRAQ and iTRAQ)

Gel-based approach Gel-free-based approach Fig. 4. A network visualizing the correlation between the potential protein markers yielded for oral and systemic pathophysiological conditions identified using gel-based and gel-free approaches. Each edge represents a statistically significant correlation between a pair of parameters, with corrected p-values b0.05. The node color represents diseaserelated up- (green) and down-regulated (red) proteins. The network was constructed using the Cytoscape platform.

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or enzymatic incorporation during proteolysis (reviewed in [128,129]). In all these approaches, stable isotopes, e.g. 2H, 13C, 15N and 18O, are used since they do not interfere with protein's chemical properties. Briefly, digested proteins from different physiological states are labeled with a light isotope and heavy isotopes being then mixed before 2D-LC-MS/MS analysis. Once labeled peptides present similar physicochemical properties, they are eluted at the same retention time during the 2D-LC analysis, but they are distinguished by mass spectrometry based on their mass difference [122,123]. Recently, more attention has been given to label-free approaches, which rely on the presumption of a linear proportionality between peptide mass peak signal intensities and peptide concentration for any given peptide [128]. In label-free quantification, biological samples are analyzed separately and relative quantification is based on the comparison of related peptide peak areas between runs. These approaches are simple and cost-effective since the extra preparation required for labeling is eliminated and can be applied to an unlimited number sample, irrespective of their origin [128]. Furthermore, they showed a high reproducibility and linearity for comparison of either peptide levels or protein abundances [130]. As can be observed in Fig. 2, iTRAQ, 18O and label-free are the approaches more used in salivary proteome analysis aiming the evaluation of glandular secretions contribution [98], aging [131], diet [111,132] or comparison of different pathophysiological conditions (recently reviewed by Kawas et al. [133]). In one of the first comparative analysis performed using iTRAQ, the effect of diurnal variation on the composition of human parotid saliva was assessed and 50 peptides were identified and quantified by MS/MS [38]. Other authors used the same approach for saliva proteome profiling in oral cancer [134], oral chronic graft [135] or breast cancer [124,136]. In line with this goal but using label-free strategy, 65 and 52 proteins were identified in different levels between healthy vs. patients with type 2 diabetes mellitus, and healthy vs. edentulous type 2 diabetes mellitus individuals, respectively (Table 3) [137,138]. Nevertheless, a deeper high-throughput analysis, in a large

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scale population, is required for the translation of those potential targets identified in these studies to clinics. Regarding salivary peptidome, efforts have been done using labelfree quantitation to evaluate the expression of the major salivary peptides (statherin, cystatins, PRPs, SMR3B (P-B peptide) and histatins) in different conditions. For instance, statherin, SMR3B (P-B peptide), and histatins were found in significant low levels in the saliva of type 1 diabetic subjects while the concentration of α-defensins 1, 2 and 4 and S100A9 was higher (Table 3) [12]. In the comparative analysis of salivary phosphopeptidome, phosphorylated forms of statherin, histatin 1 and acidic PRPs were found in significant lower levels in autistic patients [139]. Considering all the proteins identified so far as being differentially expressed in pathophysiological conditions, an interaction analysis was performed using Cytoscape (v2.8.3) for network visualization. Clusters were made based on disease-related protein levels and the methodology used in saliva analysis. As can be observed in Fig. 4, no clear association between the methodology used for saliva analysis and disease-related protein expression was noticed. Looking for disease-related protein expression, some findings can be retrieved. For instance, several proteins including cystatin S, cystatin C, amylase, defensins [1,2] and statherin seem to be similarly modulated by diseases like HNSCC, dental caries, SS, diabetes and periodontitis. However, the down-regulation of cystatin S in different pathophysiological conditions seems to be related with impaired oral cavity healthy status. An opposite effect was observed for actin, which was found up-regulated in SS, HNSCC and OSCC suggesting no disease specific association. Overall, data highlight that the identification of specific disease biomarkers remain a challenge. Approaches based on salivary peptidome analysis might give new insights in the discrimination of different pathophysiological conditions. Some authors argue that the proteolytic fragmentation pattern might be seen an individual “fingerprint” that characterize the physiological

Fig. 5. ClueGO analysis of differentially expressed proteins in saliva from healthy human donors (green nodes) and head-and-neck cancer (HNC) patients (blue nodes). Functionally grouped network are linked to their biological function. The node size represents the enrichment significance. Functionally related groups partially overlap. Not grouped terms are shown in white. The color gradient shows the proportion of each cluster. Equal proportions of the two clusters are represented in white.

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status. Salivary peptidome analysis of type 1 diabetic patients, focused on all non-salivary gland protein fragments identified, evidenced a bulk of collagen and extracellular matrix proteins-derived fragments, namely from collagen type I [11]. Salivary peptidome analysis of 10 patients with head neck cancer (HNC) resulted in the identification of 1,834 fragments belonging to 289 unique proteins, more 158 different proteins than in healthy subjects. The qualitative analysis of those identified proteins in terms of the molecular function performed with the bioinformatic tool (ClueGO [140]) in Cytoscape, highlighted the prevalence of proteins involved in the regulation of gene expression, extracellular matrix organization and tissue development in HNC patients (Fig. 5; data not published). This preliminary study open new perspectives of saliva peptidome profiling addressed to the unraveling of novel potential biomarkers for pathophysiological conditions. The HNC-related prevalence of proteins from transcription, mRNA processing, gene expression regulation and metabolism in the saliva highlights the proliferative phenotype of tumor cells. No notorious expression differences were noticed for proteins involved in the adhesion and extracellular organization, mainly represented by fragments from distinct collagen types. Future directions Despite the major technical advances observed in the last 10 years in the salivary proteomics research field, there is an overall feeling that efforts must be done towards the standardization of sample collection, pretreatment procedure and protein broad range analysis for the successful screening of a large population set in order to promote saliva as a fluid of choice for diagnostic purposes. Albeit the tremendous progresses already achieved in saliva proteome characterization, the comparative profiling in different pathophysiological conditions is still in childhood era if compared to other bodily fluids. By the employment of all the above-referred quantitative proteomic platforms, it is predictable in the near future a better characterization of modified proteins as well as a burden of novel potential disease biomarkers, which validation and specificity using orthogonal platforms is crucial for their potential clinical application. The development of bioinformatic tools to help handling with the identified 3,000 saliva protein species to disclose their biological role is predicted not only for basic research purposes that will help the development of therapeutic targets but also aiming the definition of specific diagnosis and prognosis biomarkers. Conflict of interest The authors have declared no conflict of interest. Acknowledgment This work was supported by Portuguese Foundation for Science and Technology (FCT) [grant number PEst-C/QUI/UI0062/2011]. References [1] Denny P, Hagen FK, Hardt M, Liao L, Yan W, Arellanno M, et al. The proteomes of human parotid and submandibular/sublingual gland salivas collected as the ductal secretions. J Proteome Res 2008;7(5):1994–2006. [2] Wilmarth PA, Riviere MA, Rustvold DL, Lauten JD, Madden TE, David LL. Two-dimensional liquid chromatography study of the human whole saliva proteome. J Proteome Res 2004;3:1017–23. [3] Hu S, Loo JA, Wong DT. Human saliva proteome analysis. Ann N Y Acad Sci 2007;1098:323–9. [4] Hu S, Xie Y, Ramachandran P, Ogorzalek Loo RR, Li Y, Loo JA, et al. Large-scale identification of proteins in human salivary proteome by liquid chromatography/ mass spectrometry and two-dimensional gel electrophoresis-mass spectrometry. Proteomics 2005;5:1714–28. [5] Xie H, Rhodus NL, Griffin RJ, Carlis JV, Griffin TJ. A catalogue of human saliva proteins identified by free flow electrophoresis-based peptide separation and tandem mass spectrometry. Mol Cell Proteomics 2005;4:1826–30.

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