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Following the sequencing of the human genome, the mapping of the human proteome is the next task to being completed in order to gain knowledge on how proteins are involved in disease genesis, growth, therapy, and healing. As contrary to the genome, which is relatively static, the human proteome is significantly more complex and highly dynamic. Whilst the majority of the research is being focused on analyzing either the proteome of tumor tissues and tumor cells or the proteome of serum and plasma, little attention has been awarded to the analysis of proteomes in saliva or urine. The proteome in saliva can help providing important information on processes involving health issues in dentistry, head and neck cancers, gastric cancers or neurology, to name just a few. However, this is changing and the proteomics research community is increasingly focusing on deciphering the salivary proteome. So far, more than 3000 proteins have been identified in different studies and more is to come with new instrumentation and methods available. Some of the proteomics methods applied for analysis of salivary proteins will be discussed in this chapter.
Medical University of Vienna, Clinical Department of Laboratory Medicine, Vienna, Austria
*Address all correspondence to: goran.mitulovic@meduniwien.ac.at
1. Introduction
The raise of proteomics and the continuous development and improvement of analytical instruments such as high-performance liquid chromatography (HPLC) and mass spectrometry (MS) have substantially fuelled the development of instrumental methods for analysis of proteins for both research and clinical questions [1, 2, 3, 4, 5, 6]. Proteomics is not only addressing the efficient separation of peptides upon, mostly, tryptic digestion of proteins and their sensitive detection using mass spectrometry. Proteomics is a technology enabling significantly and profoundly better approach to investigating and understanding proteins’ function and their posttranslational modifications [7, 8, 9, 10, 11, 12]. Applying proteomics methods for analysis of clinical samples is especially important in time of personalized medicine, which tailors individualized treatment of each patient based on specification of the diseases [3, 13, 14, 15, 16, 17, 18, 19]. Furthermore, (clinical) proteomics can be used as a method-of-choice for the screening of biomarkers used for early discovery and early diagnostics. Early diagnostics and early discovery, needless to say, will help decreasing patients’ morbidity and mortality by detecting the disease at the stage when it can be effectively treated with less side effects and at significantly lower cost for the society. This approach can be very beneficial especially for diseases affecting large populations such as cardiovascular diseases, diabetes mellitus and other endocrinal diseases, glioblastoma and similar.
Proteomics can also be applied for the point-of-care diagnostic approaches where both medical professionals and patients can get rapid information and bed-side diagnosis. Of course, classic proteomics approaches with protein extraction from tissue or body fluids and overnight protein digestion cannot be applied; however, proteomics can provide information to be used with kits for point-of-care approach [20, 21, 22, 23, 24].
It has already been shown that saliva is a highly valid biological fluid that can be used for diagnostic applications [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39].
Various number of components can be identified in saliva, which provides real-time data on the patient’s condition. The substances found in saliva include but are not limited to DNA, RNA, proteins, metabolites, and microbiota from both oral and gastrointestinal origin. Sample collection is simple, cheap, and can be provided by patient at home without expensive equipment or medical personal needed on-site.
This manuscript will provide a short insight into different techniques applied for proteomics analysis of saliva starting with sample collection, protein precipitation and digestion, peptide separation and MS detection, and finally with data analysis.
For medical or biological analysis in general, the method how and when a sample is being collected is of utmost importance. Sample collection is one of the pre-analytical steps that need extraordinary caution and that can influence, badly in most cases, the complete process of sample analysis.
Saliva is mainly composed of water; however, there are a number of other substances being present. Mucins, proteins, DNA, RNA, enzymes, sugars, cell debris, and microbiota and their secretome can interfere with test performance. Therefore, an optimized sample collection process is needed, and a researcher must carefully and cautiously prepare the sample collection step, and the patient must be educated and trained if sample is being to be collected at home. Furthermore, steps like sample storage and transport to the laboratory must also be carefully planned and executed especially when longitudinal studies of the same patient or of the patients’ groups are being performed, and especially in parts of the world where appropriate infrastructure is not always available [40, 41, 42, 43, 44, 45].
Several methods can be applied for collection of saliva: (1) passive droll is the simplest approach but the saliva often has a high concentration of mucins and high viscosity and (2) the Salivette® Systems [46, 47, 48], the Greiner Bio-One Saliva Kit, and the recently introduced RNA-Prosal [49]. All three sample collection systems have been used in the field, and publications describing their efficacy are available [50, 51, 52].
At the Proteomics Core Facility of the Medical University of Vienna Salivette® is being used for induced saliva sample collection by chewing cotton swabs. As mentioned previously, it is of great importance to carefully plan and perform sample collection. The patient or the donor must retain from consuming food, alcoholic beverage, and caffeine at least for 2 h before sample collection. Further, the patient shall briefly wash the mouth using water only. Saliva is being collected for 2 min during which the patient chews the cotton swab. This approach yields approximately 2.5 ml of saliva, which is sufficient for performing proteomics analysis. Some patients, however, need additional stimulation for saliva production and paraffin gum can be used in these cases to stimulate saliva flow and gain enough sample volume. In these cases, the use of Greiner Bio-One Kit helps obtaining more saliva than chewing the cotton swab; however, one shall be careful since this kit contains citric acid, which can lead to protein denaturation and protein loss during the sample collection.
As soon as the sample has been collected it shall be supplemented with enzyme inhibitors in order to suppress enzyme activity and protein degradation. A total protease inhibitor cocktail such as Roche’s “Complete Protease Inhibitor Cocktail®” is being added to the sample following centrifugation and removal of cellular debris and prior to storage at −80°C.
It is of extreme importance to secure reproducible sample collection procedures and properly train the patients in cases of self-sampling to avoid sample contamination and alteration. Furthermore, conditions for proper sample transportation and handling until it is being processed must also be carefully considered and applied.
Table 1 shows the steps applied for sample collection and the preparative work.
Step
Device
Temperature
Precautions
1. Sample collection
Salivette® (used in the Proteomics Core Facility at the Medical University in Vienna)
Human body
No food, alcoholic or caffeine beverages until 2 h before sample collection!
2. Centrifugation
Centrifuge
4°C
3. Protein precipitation
Modified Wessel-Fluegge as described or Aceton
According to the protocol described
Sample should always be prepared on ice!
4. Depletion of high abundant proteins
Antihuman serum albumin and Anti-IgG columns and anti-amylase column
Ambient to 40°C
Pay attention to columns’ loadability!
5. Enzymatic digest
Offline digest overnight
37°C
6. (Multidimensional) HPLC separation and MS detection of tryptic peptides
Nano HPLC and mass spectrometer
Various combinations
Column capacity, compatibility of selected separation dimensions
7. Bioinformatics-Database search
Various platforms are available
Not relevant
Carefully select parameters, avoid very stringent but also very lax conditions
8. Verification and Validation
Table 1.
General description of sample preparation for proteomics analysis of salivary samples.
3. Sample preparation: depletion of abundant proteins and enzymatic digest
The enzymatic digest of salivary proteins does not differ from digestion of other proteins. Usually, trypsin is being used for proteolytic cleavage of proteins due to its relatively high specificity, availability, and ease of use. Furthermore, tryptic peptides are ideally suited for reversed phase HPLC separation and positive ionization using electrospray MS (ESI). Tryptic peptides are also ideal for using multidimensional separation approaches such as strong cation exchange (SCX), hydrophilic interaction liquid chromatography (HILIC), or electrostatic repulsion interaction chromatography (ERLIC) since they bear positive charges on N-terminus or Lys-residues.
Beside trypsin, other proteases can also be used alone or in combination with trypsin. The aim is either to achieve specific cleavage of proteins for special questions or to achieve smaller peptides and enhance their ionization, detection, and ultimately better sequence coverage for identified proteins. Mostly, proteins such as Lys-C and GluC are applied for pre-digestion before trypsin addition.
Protein digest begins with protein precipitation from saliva. A number of methods have been developed for protein precipitation such as: (1) alcoholic; (2) salting out; (3) applying strong acids (trichloroacetic acid); (4) using acetone; (5) using acetonitrile, etc. Protein precipitation shall help for removing DNA and RNA and their fragments from the sample and for removing lipids.
The Proteomics Core Facility of the Medical University in Vienna applies a modified Wessel-Fluegge [53] method for protein precipitation [54]. Figure 1 shows the sample collection steps prior to protein reduction, alkylation, and addition of the protease.
Figure 1.
Sample collection steps prior to protein reduction, alkylation, and addition of the protease as performed at the Proteomics Core Facility of the Medical University of Vienna (https://www.sarstedt.com/en/products/diagnostic/salivasputum/product/51.1534.500/).
In addition to trypsin, the use of proteases, e.g. LysC, will help generating smaller peptides prior to separation and detection [55, 56].
Unlike serum or plasma, saliva does not contain large range and amounts of high abundant proteins such as serum albumin or hemoglobin that can affect sensitivity and selectivity of detection. However, amylase and serum albumin are still proteins with the highest abundance in saliva and can also affect the detection of other low abundant species and should be removed from the sample [57, 58]. In human saliva, alpha-amylase makes about 60% of the abundance of all proteins present, and its removal will help identifying proteins of lower abundance such as cytokines, which can be used as putative biomarkers for different processes. Deutsch et al. [57] have shown a simple yet very effective method for removing alpha-amylase and gaining a deeper insight into saliva’s proteome. The use of a simple potato starch resulted in sixfold reduction of the amount of alpha-amylase in the sample. Albumin removal can be facilitated by using a number of columns developed and based on immunoaffinity reactions [59].
Enzymatic digest of salivary proteins does not differ from enzymatic digests used for other biological samples. Based on previous experience and results, trypsin is the most widely used protease for protein digest resulting with peptides suited for subsequent separation using cation exchange columns or anion exchange columns, reversed phase separation, and mass spectrometric detection. A comparison of results obtained using different digestion methods for salivary samples obtained from healthy donors is shown in Figure 2.
Figure 2.
Comparison of the number of identified proteins from human saliva by applying different proteases. Courtesy of Zofia Świątczak (Master Thesis).
As shown, the highest number of proteins was identified upon applying a combined digestion approach and using GluC and trypsin. However, the choice of proteases used also depends on analytical problem to be addressed as, e.g. for detection of glycosylated proteins, which might require additional proteases to be applied.
4. Chromatographic separation of digested proteins
Upon tryptic digestion of proteins, resulting peptides are being separated on a chromatographic column prior to mass spectrometric detection and subsequent bioinformatics analysis. Separation of peptides is being performed either using one-dimensional approach or the multidimensional separation by combining two or more separation technologies prior to MS detection and analysis.
For the one-dimensional chromatographic separation approach, peptides are being injected onto the reversed phase nano HPLC column where they are separated according to their hydrophobic interaction with the stationary phase [60]. Figure 2 shows an exemplary base peak chromatogram (BPC) for one-dimensional analysis separation of salivary peptides. Usually, long separation gradients are selected for one-dimensional separation in order to provide the best possible conditions for peptide separations and large number of identifications.
The separation shown in Figure 3 was performed using a 180-min gradient and a total analysis runtime of 210 min. Thus, the total amount of available time must be considered when performing this kind of analysis.
Figure 3.
Base Peak Chromatogram (BPC) of GluC-tryptically generated peptides, which were separated on a reversed phase nano HPLC column.
The use of multidimensional separation methods will increase the number of peptides detected and the number of identified proteins, and, in addition, protein's sequence coverage will be improved for identified proteins. Improved sequence coverage for identified proteins is one of the major challenges for proteomics analysis with high importance since it enhances chances for detection of posttranslational modifications (PTM) such as phosphorylation, glycosylation, methylation, etc., which are important as drug targets.
Different types of separation approaches can be used for the two-dimensional approach: strong cation exchange columns, weak anion exchange columns, reversed phase columns at high pH, and other combinations are possible [61, 62, 63]. A schematic of possible combinations of chromatographic approaches is shown in Figure 4.
Figure 4.
A number of combinations of different techniques can be used for separation of peptides and proteins in a proteomics approach.
Generally, the use of multidimensional separation will result in increased number of identifications, and Figure 5 shows the comparison of the number of detected proteins upon applying the two-dimensional chromatographic separation with strong cation exchange column used for the first separation dimension.
Figure 5.
Two-dimensional separation approach for salivary peptides enables identification of higher number of proteins. Data courtesy of Zofia Świątczak (Master Thesis).
5. Mass spectrometric detection and bioinformatics analysis
Upon separation, peptides are being detected using mass spectrometry and analyzed by comparing experimental data and databases of in-silico digested proteins. Several MS approaches have been applied for detection of salivary peptides: Electrospray Time-of-Flight (ESI-ToF), MALDI-Time-of-Flight (MALDI-ToF), ESI-Orbitrap analysis, ESI-Quadrupole ToF, etc.
Depending on MS type and selected instrumental method, posttranslational modifications of proteins can also be identified and thoroughly analyzed thus enabled a deeper insight into the proteome. The majority of top-down analysis, i.e. analysis of undigested proteins is performed using MALDI mass spectrometers, and the majority of analysis for digested proteins (peptides) is performed using electrospray (ESI) ionization and ToF and Orbitrap mass analyzer.
The analysis of obtained raw data is performed by searching protein databases such as SwissProt, Uniprot, NR (by NCBI), and user-generated databases. A number of commercially available software packages such as Mascot (Matrix Science, London), ProteinScape (Bruker, Germany), Proteome Discoverer (Thermo Scientific, Bremen, Germany), and of free available software such as The Global Proteome Machine (www.thegpm.org), MaxQuant (http://www.coxdocs.org/doku.php?id=maxquant:start), PeptideShaker (http://www.uib.no/en/rg/probe/65218/peptideshaker), Skyline (https://skyline.ms/project/home/begin.view?), OpenMS (https://www.openms.de/), and other packages. The choice of the software to be used strongly depends not only on personal preferences but also on data to be analyzed and the information needed to be extracted.
Figure 6 shows a screenshot of two software packages preferably used at the Proteomics Core Facility at the Medical University of Vienna, Peptide Shaker and ProteinScape.
Figure 6.
Screenshots showing analysis of a salivary sample by applying two distinct software packages. Note that identified proteins have been listed based on their scores, which can be calculated using different algorithms.
In addition to database search and protein identification, the analysis of the pathways where proteins are being over- or underexpressed and the analysis of interactions with other proteins have been performed using free software such as DAVID® (https://david.ncifcrf.gov/), STRING (https://string-db.org/), Reactome (http://reactome.org/) or commercially available MetaCore® (http://lsresearch.thomsonreuters.com/) or similar.
An exemplary result of the pathway analysis of a salivary sample using DAVID® is shown in Figure 7.
Figure 7.
Pathway analysis of salivary proteins using DAVID® resulted in expected output and identification of salivary secretion as the pathway with the highest number of expressed proteins. Data courtesy of Zofia Świątczak (Master Thesis).
Finally, the analysis of all generated data and extracted information shall enable detection of putative biomarkers for diseases and therapy monitoring.
6. Application of salivary proteomics analysis for clinical research
As already mentioned, analysis of the salivary proteome can be applied to study a large area of conditions and diseases. The most intensively studied area of saliva as a diagnostic tool was its appliance for dental [21, 57, 64, 65], oral cancer [31, 66], diabetes [67, 68] or gastric cancer [58]. Furthermore, salivary proteomics was also applied for studying neurological and psychiatric disorders [69].
6.1. Application of salivary proteomics for diagnostics of oral diseases
The use of proteomics for diagnostics and treatment of oral diseases has been described in a number of publications. Jancsik et al. [70] describe the use of salivary proteomics to identify squamous oral cancer in diabetes patients. Authors have performed an additional sample homogenization, which is rarely described in other approaches to analyze saliva. Following analysis was performed by applying 2D gel separation of proteins and MS analysis using MALDI-TOF without the previous chromatographic separation and fractionation of proteins. It is known that inflammatory processes have a well-documented carcinogenetic role. Patients suffering from type-2 diabetes have also a higher risk of inflammatory diseases in the gastrointestinal tract such as ulcerative colitis or Crohn’s disease. These patients have also a higher risk of developing gastrointestinal cancer. It was shown that the incidence of developing benign tumors, leukoplakia, and malignancies was significantly increased in the group of patients with diabetes than in the healthy control group. The authors have shown a discovery of several putative biomarkers such as, e.g. Annexin A8-like, Annexin A8-like 1, Tyrosine kinase, AX969656, Protein kinase, Peroxiredoxin-2, and Annexin A2. Annexins are known to be overexpressed in colorectal cancer but also to have altered in tumorigenesis in several types of tumor. Furthermore, loss of Annexin A1 has been found to be an early event in esophageal squamous cell carcinoma. Obviously, these results show that diabetic patients have a higher risk of developing esophageal squamous cell carcinoma than the control healthy group and close monitoring shall be applied for early detection.
Delaleu et al. [71] have performed a particularly interesting and thorough investigation of the salivary proteome from patients suffering from Sjörgen’s syndrome. Salivary proteome was analyzed using a 187-plex capture antibody-based assay, and the salivary proteomic biomarker profiles were generated from patients with primary Sjörgen’s syndrome, patients with rheumatoid arthritis, and from asymptomatic controls. Authors were able to characterize putative biomarkers by detecting significant changes in 61 and 55 proteins, respectively, in samples of patients compared to that of donors without the diagnosis of Sjörgen’s syndrome. Authors were able to detect, based on 4-plex and 6-plex biomarker signatures, markers including interleukin-4 (IL-4), IL-5, and clusterin. Accurate prediction of an individual’s group membership was achieved for at least 94% of cases.
Winck et al. [72] analyzed the salivary proteome in order to decipher the immune response in oral cancer based on the salivary proteome and the extracellular vesicles isolated from saliva. The authors were able to identify significant differences in processes related to inflammatory and humoral immune responses, to peptidase inhibitor activity, iron coordination, and protease binding. Based on identifications achieved, the two classes of individuals (healthy versus patients with Oral Squamous Cell Carcinoma) were distinguished with 90% accuracy based solely on the proteomics data. The authors have used the label-free approach to quantify the identified proteins. Although both groups of peptides share the great majority of identified proteins, some identified proteins were present only in the healthy or only in the group diagnosed with cancer. Authors described that out of many differentially expressed proteins, only the protein peptidyl-prolyl cis-trans isomerase A (also known as cyclophilin-A) was statistically significant in the analysis of the mean survival time of patients, with reduced abundance of PPIA being a factor that may predict poor prognosis of OSCC patients.
6.2. Application of salivary proteomics for diagnostics of diabetes
Sedentary lifestyle paired with unhealthy food, environmental derogation, and stress situations have led to significant increase in diabetes patients worldwide. Early detection of biomarkers would enable more efficient therapy and possible delay of the diseases onset or even a prevention of the outbreak. Caseiro et al. [67] have described the use of proteomics to study Diabetes mellitus (DM) type-1. Authors have performed a quantitative proteomics analysis using the chemical proteomics approach and chemical labeling using iTRAQ. Here, sample from patients diagnosed with diabetes and from healthy subjects was pooled and processed prior to LC-MS/MS measurement and bioinformatics analysis of generated data. In addition to performing iTRAQ labeling and quantitation, authors have chosen for separating peptides by applying two-dimensional separation using high pH reversed phase chromatography. It is remarkable that authors also identified endogenous salivary peptides that are mostly ignored using MALDI-TOF and combined the results to identify more than 400 proteins. Authors used the data obtained to evaluate protein expression for patients with retinopathy, nephropathy, and no complications with the salivary proteome of healthy donors. Identification of the bactericidal/permeability increasing protein-like 1 (BPI) and pancreatic adenocarcinoma upregulated factor (PAUF) in the saliva of all diabetics clearly suggests that the activation of the immune system in type 1 DM is the most prominent process. One of the proteins, BPI, is an essential component of the innate immune system with bacteriostatic and bactericidal effects against gram-negative bacteria through lipopolysaccharides binding. The PAUF is an endogenous ligand of Toll-like receptor 2 (TLR2) and Toll-like receptor 4 (TLR4), and it is also involved in the inflammatory response, which seems to be more pronounced and more prominent in patients with retinopathy and nephropathy considering the high salivary levels of alpha-2-macroglobulin, defensin alpha 3 neutrophil-specific, leukocyte elastase inhibitor, matrix metalloproteinase-9, and neutrophil elastase.
As authors also performed analysis of the peptidome, interesting data were obtained that supported the hypothesis that Diabetes Mellitus (DM)-related proteins have higher susceptibility to proteolysis and evidenced an increased content of some specific protein fragments in saliva, which have been shown to be related with bacterial attachment and the accumulation of phosphopeptides. Especially, the accumulation of phosphopeptides seems to be involved in tooth protection against erosion and the level of their expression and phosphorylation might be a measure for dental damage that can be sustained in diabetes patients. The proteolytic fragments from bPRP1, bPRP2, and aPRP, in particular, might be considered for monitoring the disease pathogenesis with potential use for as early detection markers.
Rao et al. [73] applied multidimensional HPLC-MS/MS proteomics analysis for investigation of salivary proteins in patients with Diabetes Mellitus type-2. A Strong Cation Exchange separation of tryptic peptides from saliva of diabetic, prediabetic, and healthy subjects was performed prior to reversed phase separation and MS/MS detection. More than 400 proteins were identified and characterized and label-free quantitation was applied. As with other analysis, proteins involved with metabolic and inflammatory processes were detected in the saliva of subjects with diabetes. An important finding of the study was achieved in the prediabetic saliva: Salivary biomarkers of established Diabetes Mellitus were identified by proteomic profiling to be also differentially abundant in the saliva of patients with impaired glucose tolerance (IGT) alone and IGT + IFG (impaired fructose tolerance). These results were further confirmed by direct Western immunoblot and ELISA analyses.
The authors showed that the relative increase of some of these putative markers is associated with progression of prediabetes to the diabetic state. Therefore, systematic analysis of these putative biomarkers in prediabetic saliva, as well as their variability in individual samples, by immunoassays is of extreme importance for early acting and treatment of patients, which can prevent cardiovascular complications and mortality in diabetes patients.
An important aspect of patients suffering from DM type-2 is the severe retinopathy that can lead to blindness. Chee et al. [74] performed quantitative proteomics analysis of salivary proteins from patients diagnosed with DM type-2 without retinopathy, which served as controls and patients with DM type-2 and retinopathy. Quantitative proteomics analysis was performed by applying iTRAQ labeling and peptide separation before MS/MS detection was performed on a 50-cm nano column. Authors identified more than 300 proteins but have selected only the fully labeled pairs for quantitative analysis, a total of 119 proteins. Authors identified that increased proteins were predicted to be defense proteins and metabolic proteins suggesting that the expression of salivary defense and metabolic proteins is related to diabetic retinopathy. These results confirmed the report by Fernandez-Real et al. [75] that defense response proteins were elevated in type-2 diabetic patients and this gradually led to surging of metabolic proteins.
6.3. Application of salivary proteomics for psychiatric and neurological diseases
Fields of neurology and psychiatry urgently need new biomarkers for objective and earlier diagnoses of conditions attributed to the central nervous system (CNS). Proteomics and other “omics” technologies are being increasingly applied for these discoveries. Henskens et al. [76] performed analysis of salivary proteins already in 1996 for patients treated with different medications for epilepsy. This work was not performed by using nowadays technology, however, several salivary proteins in saliva from epileptic patients, who were medicated with different antiepileptic drugs (namely phenytoin, valproate, and carbamazepine), were found to be increased and were compared with protein levels in the saliva of healthy control subjects. It was also found that, for all patient groups, the specific amylase activity was increased up to twofold. On the other side, absolute and relative concentrations of cystatin S were diminished in all samples, but particularly strong in patients using either valproate or phenytoin. These data suggest that use of antiepileptic drugs over long periods may cause a decrease of salivary proteins such as sIgA and cystatins, which are involved in the protection of the oral cavity against microbial infections and, therefore, these patients suffer more complications related to gingiva and oral cavity in general.
Ngounou Wetie et al. [77] have investigated the use of salivary proteins as possible markers for early onset of Autism Spectrum Disorders (ASDs). Authors have identified increased levels of apolipoproteins apoA1 and apoA4 and of serum paraoxonase/arylesterase 1 (PON1) in ASD sera compared to healthy controls in blood serum and have tested the hypothesis that levels of these peptides might also be elevated in saliva. Authors found statistically significant differences in expression of a number of salivary proteins such as elevated prolactin-inducible protein, lactotransferrin, Ig kappa chain C region, Ig gamma-1 chain C region, Ig lambda-2 chain C regions, neutrophil elastase, polymeric immunoglobulin receptor and deleted in malignant brain tumors 1. Identifications made support the hypothesis that immune system disturbances may be present in individuals with ASDs.
Castagnola et al. [78, 79] have applied a proteomics approach for studying the naturally occurring peptidome of human saliva in children diagnosed with ASDs. The study revealed that naturally occurring peptides in the saliva of children with ASD can bear multiple phosphorylations. The phosphorylation level of four specific salivary phosphopeptides, identified in this study, statherin, histatin 1, and acidic proline-rich proteins for both entire and truncated isoforms, was found to be significantly lower in autistic patients, with hypophosphorylation of at least one peptide observed in 18 ASD subjects (66%). Authors suggest that different phosphorylation and hypophosphorylation of salivary peptides suggest potential asynchronies in the phosphorylation of other secretory proteins. These proteins could be relevant in the development of central nervous system during embryonic development or in early infancy. Furthermore, obtained results suggest that naturally occurring salivary phosphopeptides might help to detect and discriminate a subgroup of ASD patients.
6.4. Application of salivary proteomics for dentistry
Saliva has a continuous and intensive contact and interaction with human teeth and plays an important role in cleaning the tooth surface and antimicrobial defense. Salivary proteomics and its role in dentistry have been studied in a number of experiments. Some of these have addressed the role of salivary proteins in edentulous patients diagnosed with DM. Byrd et al. and Border et al. [80, 81] have addressed the problem of edentulous patients with Denture stomatitis (DS) and DM type-2.
Denture stomatitis refers to an inflammatory condition of the mucosal tissue underneath the denture, which could lead to severe health problems. Clinical classification of DS distinguishes three types: type 1 (DS I), type 2 (DS II), and type 3 (DS III), referring to clinically localized mild, localized moderate, and generalized tissue inflammation [80]. Authors have performed a quantitative proteomics analysis based on label-free quantitation and using two different MS platforms—an Orbitrap instrument and a Time-of-flight instrument. Interestingly, proteins were detected as differentially expressed between the two LC/MS systems. Protein expression was also different depending on the severity of DS. Authors have observed different levels of protein expression between different stages of DS and between the number of identified and quantified proteins for different disease stages and have identified serum proteins in the saliva of patients with DS III, e.g. ceruloplasmin, hemoglobins, serotransferrin, and albumin, which suggest that DS III patients experience higher level of inflammation and protein leakage from blood into saliva.
An interesting approach has been undertaken by Kaczor-Urbanowicz et al. [82] to study tooth absorption during an inflammatory process caused by orthodontic tooth movement. The orthodontically induced inflammatory root resorption (OIIRR) occurs as a consequence (the most prevalent and unavoidable) of orthodontic tooth movement. Authors have applied 2D-gel separation for salivary proteins upon depletion of abundant proteins (amylase, serum albumin, and IgG), and separated the tryptic peptides using HPLC followed by MS/MS detection. Identified proteins were quantified using the label-free approach. Authors were able to identify more than 700 proteins, which were revealed by quantitative MS of which different numbers were identified for different groups of patients. The strength of this study lays with the depth of the analyzed proteome and the significance of the results. Authors performed the bioinformatic analysis for proteins, which were found to be more than threefold increased. Different patient groups revealed different results although they all have been diagnosed with the tooth resorption. The moderate-to-severe root resorption young group revealed 38 functional clusters associated with acute and dynamic processes and in the moderate-to-severe root resorption adult group, other 16 functional clusters were found and those were related to less dynamic and slower processes. For the young group, these processes included the regulation of acute inflammatory response, defense response, response to stress, response to wounding or healing as opposed to apoptosis, glycoproteins expression, cell adhesion, signal peptides, etc. in the adult group with moderate-to-severe processes. Finally, a number of new putative biomarkers were identified, and these might be used to produce a clinical test that would serve along with radiography to perform a fast and more reliable diagnosis.
In summary, salivary proteomics is an upcoming approach for both basic and clinical research with a significant potential for use in fast diagnostic approaches. Not only the analysis of salivary proteins but also the analysis of endogenous peptides in saliva and their posttranslational modifications shall be addressed and targeted. Although a number of studies have been published and more are to come, more research is required to validate the discovered putative biomarkers so far.
Current proteomics approach for the analysis of oral fluid is not yet suited for daily routine in clinical diagnostics. However, it can help discovering biomarkers for which immunological tests such as ELISA can be developed.
Furthermore, it is essential to develop and curate a comprehensive database for the salivary proteome and establish standard conditions for sample collection and processing until the MS analysis.
The space was scarce in this chapter to address more of the clinical approach of salivary proteomics, but the researcher is encouraged to stay focused and follow the further development.
Current development shows that, without any doubt, this process will continue and will yield more biomarker candidates in the future.
References
1.Bianchi L, Gagliardi A, Landi C, Focarelli R, De Leo V, Luddi A, et al. Protein pathways working in human follicular fluid: The future for tailored IVF? Expert Reviews in Molecular Medicine. 2016;18:e9
2.Huang G, Zhou C, Wei C-J, Zhao S, Sun F, Zhou H, et al. Evaluation of in vitro fertilization outcomes using interleukin-8 in culture medium of human preimplantation embryos. Fertility and Sterility. 2017;107(3):649-656
3.Corbo C, Cevenini A, Salvatore F. Biomarker discovery by proteomics-based approaches for early detection and personalized medicine in colorectal cancer. Proteomics—Clinical Applications. 2017;11(5-6):n/a
4.Nkuipou-Kenfack E, Zürbig P, Mischak H. The long path towards implementation of clinical proteomics: Exemplified based on CKD273. Proteomics—Clinical Applications. 2017;11(5-6):n/a
5.Cifani P, Kentsis A. Towards comprehensive and quantitative proteomics for diagnosis and therapy of human disease. Proteomics. 2017;17(1-2):160079
6.Kasprzyk J, Stępień E, Piekoszewski W. Application of nano-LC-MALDI-TOF/TOF-MS for proteomic analysis of microvesicles. Clinical Biochemistry. 2017;50(4-5):241-243
7.Vaudel M, Sickmann A, Martens L. Introduction to opportunities and pitfalls in functional mass spectrometry based proteomics. Biochimica et Biophysica Acta (BBA)—Proteins and Proteomics. 2014;1844(1, Part A):12-20
8.Pehar M. Changes in protein expression and lysine acetylation induced by decreased glutathione levels in astrocytes. Molecular & Cellular Proteomics. 2016;15:493-505
9.Gu H. Quantitative profiling of post-translational modifications by immunoaffinity enrichment and LC-MS/MS in cancer serum without Immunodepletion. Molecular & Cellular Proteomics. 2016;15:692-702
10.Broncel M. Global profiling of Huntingtin-associated protein E (HYPE)-mediated AMPylation through a chemical proteomic approach. Molecular & Cellular Proteomics. 2016;15:715-725
11.Hutter G, Sailer M, Azad TD, von Bueren AO, Nollau P, Frank S, et al. Reverse phase protein arrays enable glioblastoma molecular subtyping. Journal of Neuro-Oncology. 2017;131(3):437-448
12.Cifani P, Kentsis A. Towards comprehensive and quantitative proteomics for diagnosis and therapy of human disease. Proteomics. 2017;17(1-2):160079
13.Oluwadara O, Chiappelli F. Biomarkers for early detection of high risk cancers: From gliomas to nasopharyngeal carcinoma. Bioinformation. 2009;3(8):332-339
14.Debald M, Wolfgarten M, Walgenbach-Brunagel G, Kuhn W, Braun M. Non-invasive proteomics-thinking about personalized breast cancer screening and treatment. The EPMA Journal. 2010;1(3):413-420
15.Wistuba II, Gelovani JG, Jacoby JJ, Davis SE, Herbst RS. Methodological and practical challenges for personalized cancer therapies. Nature Reviews. Clinical Oncology. 2011;8
16.Baum BJ, Yates JR 3rd, Srivastava S, Wong DT, Melvin JE. Scientific frontiers: Emerging technologies for salivary diagnostics. Advances in Dental Research. 2011;23(4):360-368
17.Haynes H, Camelo-Piragua S, Kurian K. Prognostic and predictive biomarkers in adult and pediatric gliomas: Toward personalized treatment. Frontiers in Oncology. 2014;4(47)
18.Li BT, Lou E, Hsu M, HA Y, Naidoo J, Zauderer MG, et al. Serum biomarkers associated with clinical outcomes fail to predict brain metastases in patients with stage IV non-small cell lung cancers. PLoS One. 2016;11(1):e0146063
19.Luhtala N, Aslanian A, Yates JR 3rd, Hunter T. Secreted glioblastoma nanovesicles contain intracellular signaling proteins and active Ras incorporated in a farnesylation-dependent manner. The Journal of Biological Chemistry. 2017;292(2):611-628
20.Tang Z, Louie RF, Kost G, Kost N. Principles and Practice of Point-of-Care Testing. 2002. Lippincott Williams & Wilkins
21.Wong DT. Salivary diagnostics powered by nanotechnologies, proteomics and genomics. Journal of the American Dental Association (1939). 2006;137(3):313-321
22.Zhang L, Henson BS, Camargo PM, Wong DT. The clinical value of salivary biomarkers for periodontal disease. Periodontology 2000. 2009;51:25-37
23.Chin CD, Chin SY, Laksanasopin T, Sia SK, Issadore D, Westervelt RM. Point-of-Care Diagnostics on a Chip. 2013. 3 p
24.Aro K, Wei F, Wong DT, Tu M. saliva liquid biopsy for point-of-care applications. Frontiers in Public Health. 2017;5
25.Pusch W, Flocco MT, Leung SM, Thiele H, Kostrzewa M. Mass spectrometry-based clinical proteomics. Pharmacogenomics. 2003;4(4):463-476
26.Huang CM. Comparative proteomic analysis of human whole saliva. Archives of Oral Biology. 2004;49(12):951-962
27.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. Molecular & Cellular Proteomics. 2005;4(11):1826-1830
28.Streckfus C, Bigler L. The use of soluble, salivary c-erbB-2 for the detection and post-operative follow-up of breast cancer in women: The results of a five-year translational research study. Advances in Dental Research. 2005;18(1):17-24
29.Al Kawas S, Rahim ZH, Ferguson DB. Potential uses of human salivary protein and peptide analysis in the diagnosis of disease. Archives of Oral Biology. 2012;57(1):1-9
30.Yoshizawa JM, Schafer CA, Schafer JJ, Farrell JJ, Paster BJ, Wong DT. Salivary biomarkers: Toward future clinical and diagnostic utilities. Clinical Microbiology Reviews. 2013;26
31.Krishna Prasad RB, Sharma A, Babu HM. An insight into salivary markers in oral cancer. Dental Research Journal. 2013;10(3):287-295
32.Kaczor-Urbanowicz KE, Martin Carreras-Presas C, Aro K, Tu M, Garcia-Godoy F, Wong DT. Saliva diagnostics—Current views and directions. Experimental Biology and Medicine. 2017;242(5):459-472
33.Sun Y, Liu S, Qiao Z, Shang Z, Xia Z, Niu X, et al. Systematic comparison of exosomal proteomes from human saliva and serum for the detection of lung cancer. Analytica Chimica Acta. 2017;982:84-95
34.Carro E, Bartolome F, Bermejo-Pareja F, Villarejo-Galende A, Molina JA, Ortiz P, et al. Early diagnosis of mild cognitive impairment and Alzheimer’s disease based on salivary lactoferrin. Alzheimer’s & Dementia (Amsterdam, Netherlands). 2017;8:131-138
35.Kochurova EV, Nikolenko VN. Estimation of expression of oral fluid biomarkers in the diagnosis of Pretumor diseases of oral mucosa. Bulletin of Experimental Biology and Medicine. 2017;163(1):87-91
36.Aqrawi LA, Galtung HK, Vestad B, Ovstebo R, Thiede B, Rusthen S, et al. Identification of potential saliva and tear biomarkers in primary Sjogren’s syndrome, utilising the extraction of extracellular vesicles and proteomics analysis. Arthritis Research & Therapy. 2017;19(1):14
37.Slowey PD. Salivary diagnostics using purified nucleic acids. Methods in Molecular Biology. 2017;1537:3-15
38.Kaczor-Urbanowicz KE, Martin Carreras-Presas C, Aro K, Tu M, Garcia-Godoy F, Wong DT. Saliva diagnostics—Current views and directions. Experimental Biology and Medicine (Maywood, NJ). 2017;242(5):459-472
39.Kaczor-Urbanowicz KE, Martin Carreras-Presas C, Kaczor T, Tu M, Wei F, Garcia-Godoy F, et al. Emerging technologies for salivaomics in cancer detection. Journal of Cellular and Molecular Medicine. 2017;21(4):640-647
40.Coucke LD, De Smet L, Verstraete AG. Influence of sampling procedure on codeine concentrations in oral fluid. Journal of Analytical Toxicology. 2016;40(2):148-152
41.Deeley K, Noel J, Vieira AR. Comparative study of five commercially available saliva collection kits for DNA extraction. Clinical Laboratory. 2016;62(9):1809-1813
42.Rapp-Santos KJ, Altamura LA, Norris SL, Lugo-Roman LA, Rico PJ, Hofer CC. Comparison of saliva collection methods for the determination of salivary cortisol levels in rhesus macaques (Macaca mulatta), Cynomolgus macaques (Macaca fascicularis), and African green monkeys (Chlorocebus aethiops). Journal of the American Association for Laboratory Animal Science : JAALAS. 2017
43.Roth R, Baxter J, Vehik K, Hopkins D, Killian M, Gesualdo P, et al. The feasibility of salivary sample collection in an international pediatric cohort: The the TEDDY study. Developmental Psychobiology. 2017;59(5):658-667
44.Chetwynd AJ, Dunn WB, Rodriguez-Blanco G. Collection and preparation of clinical samples for metabolomics. Advances in Experimental Medicine and Biology. 2017;965:19-44
45.Cohier C, Megarbane B, Roussel O. Illicit drugs in oral fluid: Evaluation of two collection devices. Journal of Analytical Toxicology. 2017;41(1):71-76
46.Groschl M, Kohler H, Topf HG, Rupprecht T, Rauh M. Evaluation of saliva collection devices for the analysis of steroids, peptides and therapeutic drugs. Journal of Pharmaceutical and Biomedical Analysis. 2008;47(3):478-486
47.Lundy FT, Lamey PJ. The recovery of proteolytic activity using the Salivette system. European Journal of Clinical Chemistry and Clinical Biochemistry: Journal of the Forum of European Clinical Chemistry Societies. 1995;33(7):443-444
48.Lamey PJ, Nolan A. The recovery of human saliva using the Salivette system. European Journal of Clinical Chemistry and Clinical Biochemistry: Journal of the Forum of European Clinical Chemistry Societies. 1994;32(9):727-728
49.Chiang SH, Thomas GA, Liao W, Grogan T, Buck RL, Fuentes L, et al. RNAPro*SAL: A device for rapid and standardized collection of saliva RNA and proteins. BioTechniques. 2015;58(2):69-76
50.Golatowski C, Gesell Salazar M, Dhople VM, Hammer E, Kocher T, Jehmlich N, et al. Comparative evaluation of saliva collection methods for proteome analysis. Clinica Chimica Acta. 2013;419:42-46
51.Jambunathan K, Galande AK. Sample collection in clinical proteomics—Proteolytic activity profile of serum and plasma. Proteomics—Clinical Applications. 2014:n/a
52.Torregrossa AM, Nikonova L, Bales MB, Leal MV, Smith JC, Contreras RJ, et al. Induction of salivary proteins modifies measures of both orosensory and postingestive feedback during exposure to a tannic acid diet. PLoS One. 2014;9(8)
53.Wessel D, Flügge UI. A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Analytical Biochemistry. 1984;138(1):141-143
54.Fichtenbaum A, Schmid R, Mitulović G. Direct injection of HILIC fractions on the reversed phase trap column improves protein identification rates for salivary proteins. Electrophoresis. 2016;37(22):2922-2929
55.Meyer JG, Kim S, Maltby D, Ghassemian M, Bandeira N, Komives EA. Expanding proteome coverage with orthogonal-specificity alpha-lytic proteases. Molecular & Cellular Proteomics. 2014
56.Brownridge P, Beynon RJ. The importance of the digest: Proteolysis and absolute quantification in proteomics. Methods. 2011;54(4):351-360
57.Deutsch O, Fleissig Y, Zaks B, Krief G, Aframian DJ, Palmon A. An approach to remove alpha amylase for proteomic analysis of low abundance biomarkers in human saliva. Electrophoresis. 2008;29(20):4150-4157
58.Xiao H, Zhang Y, Kim Y, Kim S, Kim JJ, Kim KM, et al. Differential proteomic analysis of human saliva using tandem mass tags quantification for gastric cancer detection. Scientific Reports. 2016;6:22165
59.Tarasova IA, Lobas AA, Černigoj U, Solovyeva EM, Mahlberg B, Ivanov MV, et al. Depletion of human serum albumin in embryo culture media for in vitro fertilization using monolithic columns with immobilized antibodies. Electrophoresis. 2016;37(17-18):2322-2327
60.Mitulović G, Smoluch M, Chervet J-P, Steinmacher I, Kungl A, Mechtler K. An improved method for tracking and reducing the void volume in nano HPLC–MS with micro trapping columns. Analytical and Bioanalytical Chemistry. 2003;376(7):946-951
61.Alpert AJ. Electrostatic repulsion hydrophilic interaction chromatography for isocratic separation of charged solutes and selective isolation of phosphopeptides. Analytical Chemistry. 2007;80(1):62-76
62.Hao P, Qian J, Dutta B, Cheow ESH, Sim KH, Meng W, et al. Enhanced separation and characterization of deamidated peptides with RP-ERLIC-based multidimensional chromatography coupled with tandem mass spectrometry. Journal of Proteome Research. 2012;11(3):1804-1811
63.Mitulovic G, Stingl C, Smoluch M, Swart R, Chervet JP, Steinmacher I, et al. Automated, on-line two-dimensional nano liquid chromatography tandem mass spectrometry for rapid analysis of complex protein digests. Proteomics. 2004;4(9):2545-2557
64.Moffa EB, Machado MA, Mussi MC, Xiao Y, Garrido SS, Giampaolo ET, et al. Vitro identification of Histatin 5 salivary complexes. PLoS One. 2015;10(11):e0142517
65.Fleissig Y, Deutsch O, Reichenberg E, Redlich M, Zaks B, Palmon A, et al. Different proteomic protein patterns in saliva of Sjogren’s syndrome patients. Oral Diseases. 2009;15(1):61-68
66.Wu C-C, Chu H-W, Hsu C-W, Chang K-P, Liu H-P. Saliva proteome profiling reveals potential salivary biomarkers for detection of oral cavity squamous cell carcinoma. Proteomics. 2015;15(19):3394-3404
67.Caseiro A, Ferreira R, Padrão A, Quintaneiro C, Pereira A, Marinheiro R, et al. Salivary proteome and peptidome profiling in type 1 diabetes mellitus using a quantitative approach. Journal of Proteome Research. 2013;12(4):1700-1709
68.Bencharit S, Baxter SS, Carlson J, Byrd WC, Mayo MV, Border MB, et al. Salivary proteins associated with hyperglycemia in diabetes: A proteomic analysis. Molecular bioSystems. 2013;9(11):2785-2797
69.Wormwood KL, Aslebagh R, Channaveerappa D, Dupree EJ, Borland MM, Ryan JP, et al. Salivary proteomics and biomarkers in neurology and psychiatry. Proteomics—Clinical Applications. 2015;9(9-10):899-906
70.Jancsik VA, Gelencser G, Maasz G, Schmidt J, Molnar GA, Wittmann I, et al. Salivary proteomic analysis of diabetic patients for possible oral squamous cell carcinoma biomarkers. Pathology Oncology Research: POR. 2014;20(3):591-595
71.Delaleu N, Mydel P, Kwee I, Brun JG, Jonsson MV, Jonsson R. High fidelity between saliva proteomics and the biologic state of salivary glands defines biomarker signatures for primary Sjogren’s syndrome. Arthritis & rheumatology (Hoboken, NJ). 2015;67(4):1084-1095
72.Winck FV, Prado Ribeiro AC, Ramos Domingues R, Ling LY, Riano-Pachon DM, Rivera C, et al. Insights into immune responses in oral cancer through proteomic analysis of saliva and salivary extracellular vesicles. Scientific Reports. 2015;5:16305
73.Rao PV, Reddy AP, Lu X, Dasari S, Krishnaprasad A, Biggs E, et al. Proteomic identification of salivary biomarkers of Type-2 diabetes. Journal of Proteome Research. 2009;8(1):239-245
74.Chee CS, Chang KM, Loke MF, Angela Loo VP, Subrayan V. Association of potential salivary biomarkers with diabetic retinopathy and its severity in type-2 diabetes mellitus: A proteomic analysis by mass spectrometry. PeerJ. 2016;4:e2022
75.Fernandez-Real JM, Pickup JC. Innate immunity, insulin resistance and type 2 diabetes. Trends in Endocrinology and Metabolism: TEM. 2008;19(1):10-16
76.Henskens YMC, Strooker H, van den Keijbus PAM, Veerman ECI, Nieuw Amerongen AV. Salivary protein composition in epileptic patients on different medications. Journal of Oral Pathology & Medicine. 1996;25(7):360-366
77.Ngounou Wetie AG, Wormwood KL, Russell S, Ryan JP, Darie CC, Woods AGA. Pilot proteomic analysis of salivary biomarkers in autism spectrum disorder. Autism Research: Official Journal of the International Society for Autism Research. 2015;8(3):338-350
78.Castagnola M, Scarano E, Passali GC, Messana I, Cabras T, Iavarone F, et al. Salivary biomarkers and proteomics: Future diagnostic and clinical utilities. Acta Otorhinolaryngologica Italica: Organo Ufficiale della Societa Italiana di Otorinolaringologia e Chirurgia Cervico-facciale. 2017;37(2):94-101
79.Castagnola M, Messana I, Inzitari R, Fanali C, Cabras T, Morelli A, et al. Hypo-phosphorylation of salivary peptidome as a clue to the molecular pathogenesis of autism Spectrum disorders. Journal of Proteome Research. 2008;7(12):5327-5332
80.Byrd WC, Schwartz-Baxter S, Carlson J, Barros S, Offenbacher S, Bencharit S. Role of salivary and candidal proteins in denture stomatitis: An exploratory proteomic analysis. Molecular bioSystems. 2014;10(9):2299-2304
81.Border MB, Schwartz S, Carlson J, Dibble CF, Kohltfarber H, Offenbacher S, et al. Exploring salivary proteomes in edentulous patients with type 2 diabetes. Molecular bioSystems. 2012;8(4):1304-1310
82.Kaczor-Urbanowicz KE, Deutsch O, Zaks B, Krief G, Chaushu S, Palmon A. Identification of salivary protein biomarkers for orthodontically induced inflammatory root resorption. Proteomics—Clinical Applications. 2017;11(9-10):1600119
Written By
Goran Mitulović
Submitted: 01 June 2017Reviewed: 08 November 2017Published: 20 December 2017
Open access peer-reviewed chapter
