Emerging salivary biomarkers by mass spectrometry

CCA-13641; No of Pages 8 Clinica Chimica Acta xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Clinica Chimica Acta

1

Emerging salivary biomarkers by mass spectrometry

2Q2

Qihui Wang a,b, Qiaoling Yu a,c, Qingyu Lin a, Yixiang Duan a,⁎

3 4 5

a

6

a r t i c l e

7 8 9 10 11

Article history: Received 23 June 2014 Received in revised form 26 August 2014 Accepted 29 August 2014 Available online xxxx

12 13 14 15 16 17

Keywords: Saliva Biomarker Non-invasive Mass spectrometry Disease diagnosis

a b s t r a c t

T

C

33 31 30

E

Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Specimen collection and process . . . . . . . . . . . . . . . . . . . . 2.1. Types of collected saliva . . . . . . . . . . . . . . . . . . . . . 2.2. Saliva collection protocol . . . . . . . . . . . . . . . . . . . . 2.3. Specimen process . . . . . . . . . . . . . . . . . . . . . . . . 3. Mass spectrometry based proteomics techniques for identification biomarkers 3.1. 2-DE/MS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. LC–MS/MS . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. MALDI-TOF/MS . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. SELDI-TOF/MS . . . . . . . . . . . . . . . . . . . . . . . . . 4. Validation and quantitation of proteomics for salivary biomarkers . . . . . 4.1. Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Quantitation . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Diagnostics and clinical analysis of saliva samples . . . . . . . . . . . . 5.1. Oral diseases . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Systematic diseases . . . . . . . . . . . . . . . . . . . . . . . 6. Conclusions and future perspectives . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

N C O

R

R

1. 2.

U

36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

O

P

Human saliva, a multi-constituent oral fluid, has a high potential for early diagnosis of disease. Proteomic analysis of saliva holds promise as a non-invasive method that is advantageous over serum. This non-invasive diagnostic method represents developing trends in analytical and clinical chemistry. Significant technological advances in the field of proteomics during the last two decades have greatly facilitated the research toward this direction. However, these technologies still require integration and standardization of validation against accepted clinical and pathologic parameters. In this review, a summary of mass spectrometry-based technologies of saliva biomarker discovery, potential clinical applications, and challenges of saliva proteomics have been discussed, as well as latest technologies of validation and quantification of saliva biomarkers. It is likely that the use of saliva for early diagnostics of diseases will continue to expand thus providing a new approach of instrumental investigation for physiologic and physiological states. These novel biomarkers have obvious clinical utility that will help to diagnose many diseases at early stage. © 2014 Published by Elsevier B.V.

29 32 35 34

R O

i n f o

D

c

Research Center of Analytical Instrumentation, College of Life Science, Sichuan University, Chengdu 610064, PR China College of Chemistry and Life Sciences, Chengdu Normal University, Chengdu 610041, PR China Department of Environmental and Food Engineering, Liuzhou Vocational and Technical College, Liuzhou 545006, PR China

E

b

F

journal homepage: www.elsevier.com/locate/clinchim

. . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

55 Abbreviations: WS, whole saliva; SS, Sjögrens syndrome; 2-DE/MS, two-dimensional gel electrophoresis-mass spectrometry; LC–MS/MS, liquid chromatography tandem mass spectrometry; MALDI-ToF/MS, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry; SELDI-ToF/MS, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry; LIT–Orbitrap, linear ion trap–Orbitrap; MudPIT, Multidimensional protein identification technology; SRM/MRM, Selected/multiple reaction monitoring assays; ELISA, Enzyme-linked immunosorbent assay; 2D-DIGE, 2D difference gel electrophoresis; ICAT, Isotope-coded affinity tagging; iTRAQ, Isobaric tags for relative and absolute quantification; OSCC, Oral squamous cell carcinoma. ⁎ Corresponding author at: Research Center of Analytical Instrumentation, Sichuan University, 29 Wangjiang Road, Chengdu 610064, PR China. Tel./fax: +86 28 85418180. E-mail address: [email protected] (Y. Duan).

http://dx.doi.org/10.1016/j.cca.2014.08.037 0009-8981/© 2014 Published by Elsevier B.V.

Please cite this article as: Wang Q, et al, Emerging salivary biomarkers by mass spectrometry, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.08.037

18 19 20 21 22 23 24 25 26 27 28

Q. Wang et al. / Clinica Chimica Acta xxx (2014) xxx–xxx

95 96

101

Compared with blood, saliva collection is relatively easy and costeffective [9]. It may not evoke an ethical issue in special populations. Non-invasiveness is one of the great advantages of saliva as a diagnostic medium, especially when repeated samples must be taken for particular examinations. It is convenient for the patient because samples can be collected at home. It is widely believed that different types of the collected saliva may give rise to different biomarkers.

102

2.1. Types of collected saliva

103

Saliva can be collected as whole saliva (WS) or the individual salivary gland saliva. Whole saliva includes secretions from both three major glands and minor glands as well as gingival crevicular fluid, cellular debris, bacteria, and many microbes [10]. It is most frequently studied for the evaluation of systemic diseases because of ease in collection, rapidness in obtaining and no need of specialized equipment. On the other hand, the collection of individual salivary gland saliva is much more difficult since a variety of sophisticated devices must be used. It provides controlled fluid and little influence from the other part of the oral cavity. Therefore, it primarily suits for the detection of glandspecific pathology. Saliva can also be collected as unstimulated or stimulated saliva. On average, the flow rate of unstimulated saliva is 0.3 mL/min, stimulated flow rate is, at maximum, 7 mL/min [11]. Stimulated saliva is generally

78 79 80 81 82 83 84 85 86 87 88 89 90 91

97 Q3 98 99 100

104 105 106 107 Q4 108 109 110 111 112 113 114 115 116

C

76 77

E

74 75

R

72 73

R

70 71

O

68 69

C

66 67

N

64 65

U

62 63

2.2. Saliva collection protocol

128

F

2. Specimen collection and process

61

A common protocol used for collecting saliva sample is given below. In order to collect saliva, all the subjects are asked to abstain from eating, drinking, smoking, or from using oral hygiene products for at least 1 h prior to collection. Then their mouths were rinsed thoroughly with water. 1) For unstimulated whole saliva, it is dripped from the open mouth into a collecting cup. 2) For stimulated whole saliva, place a standard quantity of paraffin in the mouth and chew at a regular pace, then expectorate saliva periodically into a disposable plastic cup for a period of 5 min [18]. 3) For parotid saliva, it can be collected with a Lashley cup or a modified Carlson–Crittenden device. 4) For submandibular and sublingual glands, use a Block and Brotman collector for submandibular and sublingual gland secretions [19].

O

94

59 60

R O

92 93

Saliva, non-invasive and stress-free alternative to blood, is widely accepted as a potential medium for clinical diagnostics. It is a readily accessible secretion that plays an important role in esophageal physiology, digestive process, gastric cell protection, and oral lubrication [1]. In addition, saliva also protects the oral cavity from foreign invaders, such as bacteria and viruses, by digestion and inhibition of their growth [2]. Therefore, saliva has attracted more and more attention. Saliva is secreted primarily by the three major glands namely parotid gland, submandibular gland and sublingual gland [3]. Generally, salivary glands generate 1–1.5 L of saliva each day [4]. It contains approximately 99% water with minerals, nucleic acids, electrolytes, mucus and proteins such as amylase, cytokines, immunoglobulins, mucins and other glycoproteins [5]. It is one of the most complex, versatile, and important body fluids, supplying a wide range of physiological needs. Therefore saliva is also called the “mirror of the body” or “a window on health status”. The idea of using saliva in medical diagnosis was made in the second half of the 20th century [6]. At present, saliva represents an increasingly useful auxiliary means of diagnosis due to the use of novel approaches including proteomics, genomics, metabolomics and bioinformatics. Additionally, it has the advantages of being simple, non-invasive, easy to store, and inexpensive compared to blood. Saliva may exchange with substances that compose blood. The mechanisms of transport of proteins and ions from blood into saliva were introduced: active transport, passive intracellular diffusion and extracellular ultrafiltration [7]. Some molecules as ligand receptor binding enter into saliva through active transport; Hydrophilic and small molecules enter saliva from blood capillaries through passive intracellular diffusion; Hydrophobic compounds enter into saliva through the gap junctions on the blood membrane (extracellular ultrafiltration). Therefore, saliva is functionally equivalent to blood in reflecting the physiological state of the body. Saliva consists of approximately 2000 proteins, and most importantly, about 597 of those proteins are also observed in the blood [8]. Therefore, salivary proteomics has demonstrated a great potential for clinical diagnosis. In this review, we describe the mass spectrometry-based methods used for identification salivary biomarkers, saliva-test applications and their potential use in clinical diagnosis of various diseases.

117 Q5 118

P

57 58

obtained by chewing on paraffin or gum, or by using citric acid or sour candy drops on the subject's tongue. In these stimulated method, citric acid elicits the largest volumes of saliva [12,13]. Unstimulated saliva represents an equilibrated condition, having less influence from salivary glands. Unstimulated saliva collection can be obtained by draining, spitting, and suctioning [14]. To date, a majority of diagnostic studies chose to use unstimulated WS [15–17]. However, stimulated WS is more suitable for diagnosis of some special diseases such as Sjögrens syndrome (SS). Because SS is a chronic disease affecting lacrimal, salivary and other exocrine glands, patients have difficulty producing enough saliva.

D

1. Introduction

2.3. Specimen process After collecting, the samples are centrifuged at 14 000 rpm for 20 min at 4 °C to remove insoluble materials, cell debris and food remnants. The supernatant is divided into 1 mL portions and frozen at −80 °C until laboratory analyses. Frozen saliva samples are then stored at − 70 °C [15], or − 20 °C [20]. Studies have indicated that storage at −80 °C are better than at −20 °C, especially for prolonged times [21], because saliva is one of the most complex body fluids, and storage at low temperature promotes peptide and protein stability. Another potential interference is high-abundance proteins, such as amylase, which may interfere in the detection of other low abundance proteins appearing in the disease state. Lower-abundance proteins such as cytokines, present at the pictograms level, may offer markers for clinical diagnostics. The immunoaffinity column can be used as a common method in preprocessing saliva in order to deplete high-abundance proteins [22]. Omer Deutsch et al. made an alpha amylase removing device through affinity adsorption to potato starch in a 1 mL plastic syringe with a 0.45 mm filter at the tip, and the removal efficiency is at least sixfold [23].

T

56

E

2

119 120 121 122 123 124 125 126 Q6 127

129 130 131 132 133 Q7 134 135 136 137 138 139 140 141 142 143 144 145 Q8 146 147 148 149 150 151 152 153 154 155 156 Q9 157 Q10 158 Q11 159

3. Mass spectrometry based proteomics techniques for identification 160 biomarkers 161 Many diseases, especially for cancer, have caused huge amounts of deaths every year. Unfortunately, most of the cancers are hard to discover in the early stages [24]. Therefore, effective saliva biomarkers are urgently needed to be identified in the use for early diagnosis of diseases. With the advanced instruments and developed refined analytical techniques, proteomic technologies are widely used as useful and powerful approaches and provide tremendous opportunities for biomarkerrelated clinical applications. Due to its particular sensitivity and highly accurate mass measurement, mass spectrometry has become one of the core technologies for proteomics. A variety of MS techniques, such as two-dimensional gel electrophoresis–mass spectrometry (2-DE/ MS), liquid chromatography tandem mass spectrometry (LC–MS/MS), matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS), and surface-enhanced laser desorption/

Please cite this article as: Wang Q, et al, Emerging salivary biomarkers by mass spectrometry, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.08.037

162 163 164 165 166 167 168 169 170 171 172 173 174 175

Q. Wang et al. / Clinica Chimica Acta xxx (2014) xxx–xxx

3.2. LC–MS/MS

209 210

Gel-based proteomics can be time consuming and reproducibility suffers when used in large scale studies of human saliva. Therefore, liquidbased separation strategies, coupled with MS, have also been applied for saliva proteomics. LC–MS/MS provides a very effective methodology for the basic “shotgun” proteomics experiment. This method also called“bottom-up” proteomics. With the development of modern mass spectrometry techniques, more advanced mass spectrometers have also been used in shotgun proteomics, such as quadruple time-of-flight [30], linear ion trap [31], and linear ion trap–Orbitrap (LIT–Orbitrap) [32]. Among these advanced equipments, LIT–Orbitrap, consisting of a linear quadrupole ion trap coupled to the Orbitrap, provides a significantly enhanced resolution in ESI-LC/MS analysis. These advanced MS/MS techniques significantly improved sample throughput and provide better confidence for biomarker identification. In addition, to identify more proteins, multidimensional protein identification technology (MudPIT) has also been used. For example, Kiran S. Ambatipudi et al. used MudPIT to identify differentially expressed parotid salivary proteins that might ultimately prove to be biomarkers for early Sjögren's syndrome detection [33]. In shotgun proteomics, the most important step is protein digestion. The reason is that the data collection relies on efficient digestion of protein [34]. A protease of specificity, trypsin, is commonly used to digest the protein. One of the trypsins produce peptides of a size more readily analyzed by MS, and the other is amino acid specificity, which produces a mass fingerprint specific enough to allow identification of the protein. One important advantage of shotgun proteomics is high-throughput, which allows many proteins to be identified at one time [35]. However, the “shotgun” method also has some shortcomings, such as incomplete digestion,

198 199 200 201 202 203 204 205

211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237

C

196 197

E

195

R

193 194

R

191 192

N C O

189 190

U

187 188

F

208

185 186

O

206 207

2-DE is a highly resolving separation technique. O'Farrell first described this technique in 1975 [25], and since then, it has been widely used over the past 40 years. In order to map out saliva proteins, proteins were separated by 2-DE followed by in-gel digestion of each gel spot and MS measurement of the resulted peptide fragments. Ghafouri et al. have detected about 600 protein spots on fluorescent stained 2DE gels. About 150 spots were picked, trypsinated and run by MS. Finally, 101 proteins were identified in saliva [26]. Hu et al. also identified 64 distinct proteins in human salivary proteome by 2-DE/MS [27]. 2-DE/MS is likely to remain in an extensive use for the foreseeable future with the advantages of simple and readily available to many laboratories. It is widely used for the separation of proteins because of high throughput (thousands of proteins per gel). This classical approach not only allows the separation of different isoforms, but also has further advantages that provide more information about protein modifications, proteolysis and produce quantitative values [28], and probing with antibodies or other biologically reactive compounds that can be performed on transfer-blots [29]. Therefore, the 2-DE proteins map constructed opens the possibility to investigate proteins change associated with disease processes. However, limitations of the current 2-DE-based proteomics technologies applied to biomarker discovery are labor and time intensive process that is difficult to automate. This approach still suffers from several technical limitations in terms of repeatability and reproducibility, and has a limited resolution for proteins with molecular weights less than 10 kDa.

R O

183 184

P

3.1. 2-DE/MS

D

182

178 179

difficult separation of peptides chromatographic etc. The first global shotgun proteomics study using LC–MS/MS was published in 2002 by Adkins [36]. Hu et al. utilized nano-LC/MS/MS strategy for large-scale identification of whole saliva proteins [27]. In addition, 2D/LC/MS and shotgun proteomics can be used for human saliva of type 2 diabetes by B. B. Michael et al. [37] and for oral cancer biomarker discovery by J.A. Kooren et al. [38]. In respect to discovery saliva biomarkers, LC–MS/MS also played an important role. Many potential saliva biomarkers have been found [27,30,38–41]. In addition, some other methods such as selected/multiple reaction monitoring assays (SRM/MRM) can be used for the analysis of proteome digests in clinical proteomics. It is an emerging technique in proteomics as the ideal tool to complement shotgun studies [42]. A protein sample of interest can be digested and analyzed in SRM or MRM mode. This method is a promising technique with the advantages of being highly selective, sensitive, and high-throughput. The high sensitivity and selectivity of LC–MS/MS makes it possible to develop rapid analysis of very low concentrations even in complex biological materials, in addition to the advantages of a relatively large dynamic range and the relatively high throughput [43]. However, it generally requires some sample preparation procedures, such as solidphase extraction, to remove coexisting substances prior to analysis. Therefore, some enrichment techniques have been developed along the way and coupled to LC–MS/MS to increase coverage of the saliva proteomes [44]. These preparation techniques not only provide better precision, but also improve sensitivity. 3.3. MALDI-TOF/MS

T

180 181

ionization time-of-flight mass spectrometry (SELDI-TOF/MS) have been extensively applied to identification biomarkers in saliva. The following sections review both established and emerging proteomic techniques used in saliva biomarker discovery, highlighting the strengths and shortcomings of each technique. We also evaluate the various methods for validation and quantification of saliva biomarkers.

E

176 177

3

238 239 Q12 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264

MALDI-TOF/MS is one of the most powerful tools. It is widely used for mass spectrometric analysis of large, non-volatile biomolecules, in particular peptides, proteins, oligonucleotides, and oligosaccharides [45]. The mechanism of desorption of proteins from the plate may best be described as a conversion of laser energy into vibrational oscillation of the crystal molecules. MALDI is a relatively “soft” ionization technique in intact high mass compound. Therefore, it is very suitable for analysis of saliva biomarkers with high-molecular weight. The application of MALDI-based methods for the analysis of proteins had for years remained a field of big impact to the saliva proteomics. MALDI-TOF/MS can be applied as a powerful tool for rapid monitoring of the composition changes in saliva, without a requirement for extensive pre-treatment [46]. It has been used to identify potential saliva biomarkers for various diseases, such as oral squamous cell carcinoma [47–49], lung diseases [50], Sjögren's syndrome [51], gastric cancer [16], Type 1 diabetes [20], hematopoietic stem cell transplantation [52]. MALDI is well suited for resolution of proteins with high-molecular weight (N100 kDa). Many researchers have demonstrated its usefulness in regard to high sensitivity and high throughput [53], short analysis time, and ease of use, which render it an effective means of monitoring biomarkers directly from saliva. However, some disadvantages, variable sample preparation for different analytes, different discriminatory peaks for similar samples, biased toward highly abundant proteins, a limited mass window range, have limited its development.

265

3.4. SELDI-TOF/MS

289

SELDI-TOF/MS is a variant of MALDI-TOF/MS in which a selected part of protein mixture is bound to a specific chromatographic surface and the rest washed away. The surface modified with a chemical functionality for binding a subset of sample proteins based on absorption, partition, electrostatic interaction or biochemical affinity on a solid-phase protein chip surface [24,54]. Bio-fluids are applied directly to a target plate that is later introduced into a mass spectrometer. In addition, the use of different ProteinChips for the separation of proteins can reduce the complexity of the sample [55].

290 291

Please cite this article as: Wang Q, et al, Emerging salivary biomarkers by mass spectrometry, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.08.037

266 267 268 269 270 271 Q13 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288

292 293 294 295 296 297 298

324 325

353

Clinical diagnostics of a disease especially cancer using saliva probably requires the analysis of a profile of biomarkers to achieve an acceptable level of sensitivity and specificity. Immunoassays, such as enzymelinked immunosorbent assay (ELISA) or western blotting, are the most frequently used methods for validation of saliva biomarkers. ELISA analysis is common method for quantifying protein levels in saliva samples in both research and clinical laboratories. This is a rapid immunochemical test that involves an enzyme. The assay involves antibody, antigen and capture antibody. Capture antibody is attached to a solid surface and serves to bind the protein biomarker onto the surface. Then the chip is incubated with a detection antibody that binds the same antigen as the capture antibody, but does so at a different site. If properly used, ELISA is a sensitive, accurate and rapid detection method. It is especially effective when large numbers of samples must be assayed. In addition, they do not need radioisotopes or a radiationcounting apparatus. ELISA can identify the levels of proteins and, thus, has the potential to accelerate validation of saliva protein biomarkers for clinical use. Nowadays, more and more saliva biomarkers have been validated by ELISA. For example, lactoferrin, IgA2 and albumin [17], M2BP, involucrin, histone H1, S100A12, and S100P [30]. Western blotting is another immunoassay method used for validating saliva biomarkers, which involves the immobilization of proteins on membranes before detection using monoclonal or polyclonal antibodies. In the method, saliva samples are separated using SDS-PAGE first, giving information about molecular weight and existence of different isoforms of the proteins. In recent years, this immunoassay method has been used to validate saliva biomarkers for different diseases [18, 30,63,64]. However, further development of these technologies is somewhat hampered due to the lack of some specific antibodies or antigens to the biomarkers.

354

4.2. Quantitation

355 356

With the development of chromatography, mass spectrometry, and bioinformatics, quantitative measurement of proteins is increasingly important as a means of characterizing complex saliva biomarkers. Some approaches including gel-based methods and non-gel-based methods are mainly discussed and summarized in this review.

330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352

357 358 359

C

328 329

E

326 327

R

318 319

R

316 317

O

314 315

C

312 313

N

310 311

U

308 309

5. Diagnostics and clinical analysis of saliva samples

408

Because of the advantages of non-invasive, inexpensive, and easyto-use, saliva plays more and more important roles in clinical diagnosis. Unlike blood, collecting saliva will not bring any suffering to patients. For clinicians, saliva collection is safer than that of venipuncture. In addition, saliva is also easier to handle. Therefore, saliva is an ideal biological fluid used as diagnostic medium. Recently, due to the combination of emerging salivary diagnostics and biotechnologies, a large number of medically valuable analytes in saliva are gradually unveiled and some of them represent biomarkers for different diseases. The above discussed approaches have potentially been applied to diagnose oral diseases and systemic diseases. As shown in Table 2, the summary of MS-based methods for analyzing saliva biomarkers is given. These biomarkers may be used as supplementary tools to diagnose many diseases, such as gastric cancer, Type-1 diabetes, breast cancer, caries, oral lichen planus, oral squamous cell carcinoma.

409 410 Q16

F

4.1. Validation

306 307

O

323

305

R O

4. Validation and quantitation of proteomics for salivary biomarkers

303 304

360 361

P

322

301 302

Gel-based methods are widely practiced and employed by many researchers in quantitative proteomics. The most frequently used method is the 2D difference gel electrophoresis (2D-DIGE) which is based on fluorescence. Due to the samples run together on the same gel, potential gel-to-gel variation can be eliminated. 2D-DIGE gives more accurate and reliable quantitative information of protein abundance [65]. However, gel-based techniques also have some disadvantages, such as labor intensive, time-consuming and low dynamic range [66]. In order to solve the problems of gel-based techniques, non-gel based quantitative proteomics techniques have been developed rapidly. Isotope-coded affinity tagging (ICAT) as a novel non-gel strategy was developed for quantitative proteomics analysis. The relative intensities of the light and heavy isotope-labeled peptides are the base of the relative quantities of the protein [67]. Although ICAT is the most commonly practiced technique in LC-based quantitative proteomics and a number of limitations including missed identification of proteins with few or no cysteine residue [68], requirement for higher sample concentration, complexity of sample preparation, and increased time have been noted in the prototypical ICAT technique [69]. Now, the new cleavable ICAT (cICAT) reagent has solved some of the aforementioned problems [30]. Isobaric tags for relative and absolute quantification (iTRAQ) is a relatively new technique, but is gaining in popularity as an alternative to ICAT. This approach has been used in many researches. De Jong et al. used iTRAQ reagents to reveal myosin and actin as promising saliva biomarkers for distinguishing pre-malignant and malignant oral lesions [70]. Streckfus et al. use iTRAQ-coupled LC–MS/MS analysis to identify salivary biomarkers for breast cancer [40]. With respect to sensitivity, three techniques (DIGE, cICAT and iTRAQ) were used to quantify reconstituted protein mixture by Wells W. Wu [67]. As shown in Table 1, the larger number of peptides detected by iTRAQ suggests that iTRAQ is more sensitive than cICAT or DIGE. Because of potential limitations of the labeling-based quantification approaches, such as high cost of the reagents, specific quantification software, incomplete labeling, and label-free proteomic techniques has drawn more and more interest [71]. Chelius et al. demonstrated linear responses of peptide ion peak areas between 10 and 1000 fmol of myoglobin spiked into human plasma with RSD b 11% [72] demonstrated that mass spectral peak intensities of peptide ions correlate well with protein abundances in complex samples. Another label-free method, termed spectral counting, compares the number of MS/MS spectra assigned to each protein. Liu et al. used this method to demonstrate a linear dynamic range over 2 orders of magnitude in yeastsoluble cell lysate [73]. Therefore, the rapid development of label-free quantitative techniques has provided faster, cleaner and simpler quantification results. Specially, the most significant difference compared with protein-labeling approaches is that each sample is separately prepared in the sample preparation step before LC–MS/MS.

T

320 321

SELDI-TOF/MS is one of the most promising new approaches for the discovery and identification of potential biomarkers of saliva for various diseases. In the context of clinical proteomic, many recent papers discussed newly found disease biomarkers [56–58]. Schipper et al. also investigated the role of some analytical and pre-analytical variables on the protein profile of saliva by SELDI-TOF/MS [21,59]. Massimo et al. defined a protocol that improved the quality and the reproducibility of SELDI-TOF/MS analysis of saliva samples [60]. The SELDI enables rapid and high-throughput (up to 96 samples per bioprocessor) detection of saliva proteins and peptides directly from crude mixtures without pre-processing. This technique has two main advantages: one is the very rapid analysis, and the other is that it can directly test native undigested biological samples. In addition, it can perform the separation, detection, and analysis of proteins from biological samples at the femtomole sensitivity, particularly appropriate for the investigation of low-molecular weight proteins (b20 kDa) [61]. Furthermore, SELDI-TOF/MS is more sensitive and requires only small amounts of sample with respect to other proteomic techniques. However, this technology is not free of criticism. In particular, poor reproducibility of the results is reported because of drift, noise, or the use of different chips [62]. It is not suitable for high molecular weight proteins (N 100 kDa), and resolution and mass accuracy lower than that of MALDI-TOF/MS.

D

299 300

Q. Wang et al. / Clinica Chimica Acta xxx (2014) xxx–xxx

E

4

Please cite this article as: Wang Q, et al, Emerging salivary biomarkers by mass spectrometry, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.08.037

362 363 364 365 366 367 368 369 370 371 372 Q14 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 Q15 396 397 398 399 400 401 402 403 404 405 406 407

411 412 413 414 415 416 417 418 419 420 Q17 421 Q18 422 423

Q. Wang et al. / Clinica Chimica Acta xxx (2014) xxx–xxx Table 1 Quantify reconstituted protein mixture by three techniques.

t1:3

Reconstituted protein mixture

t1:4

Method

t1:5

iTRAQ

t1:6

cICAT

t1:7

DIGE

t1:8 t1:9

a

Q1

1 or 3 pmol BSA in depleted plasma

10 or 30 pmol BSA in method buffer

10 or 30 pmol BSA in depleted plasma

10 92.0% 19.1% 2 121.7% 28.2% 2 83.4%

5 99.7% 14.8% 1 86.3% n/a 2 85.8%

33 93.7% 30.0% 14 92.7% 20.9% 5 92.3%

23 85.0% 27.4% 7 87.0% 14.0% 4 80.9%

t2:1 t2:2

Table 2 Summary of MS-based methods for analysis saliva biomarkers.

432 433 434 435 436 437 438 439

C

430 431

E

428 429

R

426 427

O

440

Oral cancer is the sixth-most common cancer, and more than 90% of oral cancers are oral squamous cell carcinoma (OSCC). The World Health Organization has reported this malignant tumor with 5-year mortality rate at 45%, the reason is that most of the OSCC patients are diagnosed at a late stage. Therefore, early diagnosis of OSCC constitutes the most urgent problem for oral cancer diagnostics. Recently, 64 OSCC patients and 64 control subjects were recruited for discovering differentially expressed proteins in saliva. 5 protein biomarkers (M2BP, MRP14, CD59, Profilin 1, Catalase) have been discovered by 2DE/MS and validated by immunoassays with sensitivity of 90%, and specificity of 83% in detecting OSCC [30]. Thioredoxin was also discovered as salivary oral cancer biomarkers using MALDI-TOF. Specificity and sensitivity were determined to be 70.8% and 70.8%, respectively [47]. TNF-α, another oral cancer biomarker, has a salivary concentration approximately 30 pg mL−1 in oral cancer patients and 3 pg mL− 1 in healthy individuals. Truncated cystatin SA-I (p b 0.05), with deletion

R O

425

of three amino acids from the N-terminus, has been discovered by SELDI-TOF Protein Chip system, which might be a useful tumor biomarker for OSCC [56]. Biomarkers of OSCC validated by immunoassays have been summarized in Table 3. Tongue cancer, one of the oral cancers, is among the most common and fatal types of cancers in the world. Salivary levels of IL-1a, IL-6, IL-8, VEGF-a and TNF-a, could serve as potential biomarkers for early detection of squamous cell carcinoma of the tongue [38]. Sjögren's syndrome is a chronic autoimmune disorder. It primarily affects women with a ratio of 9:1 over the occurrence in men. This disease mainly targets the exocrine glands. Analyses of parotid saliva from 20 non-SS subjects and 41 primary SS patients demonstrated that lactoferrin and β2-microglobulin in parotid saliva showed the largest increases in SS patients by SELDI-TOF/MS [58]. Nicolas Delaleu et al. found that CD40, CD40 ligand, IL-18, granulocyte chemotactic protein2 and anti-muscarinic M3 receptor IgG3 may connect the different aspects of SS [78]. In addition, Rudney et al. suggested that statherin and cystatin S be the best predictors of occlusal caries in saliva [74].

P

5.1. Oral diseases

T

424

F

Accuracy % is defined as the acquired ratio divided by the theoretical ratio. Relative standard deviation is defined as standard deviation divided by the average of the ratios for that protein.

D

b

No. of matched peptides Accuracy % a Relative SDb No. of matched peptides Accuracy % a Relative SDb No. of matched peptides Accuracy %a

1 or 3 pmol BSA in method buffer

E

t1:1 t1:2

5

Disease

Saliva

Stimulation

Proteomics approach

Biomarkers

Verification methods

Ref.

t2:4 t2:5

HNSC Gastric cancer

Whole Whole

Stim Unstim

LC–MS/MS MALDI-TOF/MS

•Western blot –

[18] [16]

t2:6 t2:7

Type-1 Diabetes

Whole

Stim

2-DE MALDI-TOF/MS

–

[20]

t2:8

OSCC

Whole

Unstim

2-DE/MS LC–MS/MS

•ELISA •Western blot

[30]

t2:9

Graft versus host disease

SM/SL

Stim

SELDI-TOF/MS MALDI-TOF/MS

•ELISA

[52]

t2:10

Type-2 diabetes

Whole

Unstim

LC–MS/MS

•ELISA

[63]

t2:11

Caries

Whole

Unstim

2-DE/MS

•Western blot

[74]

t2:12

Breast cancer

Whole

Stim

SELDI-TOF/MS

•Complement factor B •1472.78 Da •2936.49 Da •6556.81 Da •7081.17 Da •α-amylase •Cystatin •PIP •M2BP •MRP14 •CD59 •Profiling1 •Catalase •Lactoferrin •SLPI •IgA •b2-microglobulin •A1AT •Cystatin C •α-2-macroglobulin •Transthyretin •Statherin 5 •Cystatin •c-erbB-2

•ELISA •Western blot

[75]

t2:13

Pulmonary disease

Whole

Unstim

2-DE/MS

t2:14

Oral lichen planus

Whole

Unstim

2-DE MALDI-TOF/MS

t2:15 t2:16

U

N C O

R

t2:3

•Lipocalin •Apolipoprotein A1 •Urinary prokallikrein •PLUNC

[76] –

[77]

Abbreviations: stim, stimulated; unstim, unstimulated; P, parotid; SM, submandibular; SL, sublingual; HNSC, head and neck squamous carcinoma; OSCC, oral squamous cell carcinoma; SS, Sjögren's syndrome.

Please cite this article as: Wang Q, et al, Emerging salivary biomarkers by mass spectrometry, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.08.037

441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458

6 t3:1 t3:2

Q. Wang et al. / Clinica Chimica Acta xxx (2014) xxx–xxx

Table 3 Biomarkers of oral cancer verified by immunoassays. Biomarker

Up/down

Biomarker

Up/down

t3:4 t3:5 t3:6 t3:7 t3:8 t3:9 t3:10 t3:11 t3:12 t3:13 t3:14 t3:15 t3:16 t3:17 t3:18 t3:19 t3:20

Complement factor B Truncated cystatin SA-I M2BP CD59 Profilin 1 MRP14 Catalase Insulin growth factor I Metalloproteinases MMP-9 Cyclin D1 Interleukin 1β Interleukin 6 Interleukin 8 Transferrin Thioredoxin OAZ-1 SAT

Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation

Calgranulin A Transcription 3 Calgranulin B CD44 Cyfra21-1 Tissue polypeptide antigen Cancer antigen 125 ZNF510 peptide miR-31 TPS Endothelin-1 PIGR Immunoglobulin A 8-Oxoguanine DNA glycosylase Phosphorylated-Src Maspin

Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Up-regulation Down-regulation Down-regulation Down-regulation Down-regulation Down-regulation

487 488

6. Conclusions and future perspectives

510 511 512

t5:1 t5:2

D

P

present in saliva are illustrated in Table 5. In addition, Lipocalin and Apolipoprotein A1 were identified as biomarkers of chronic obstructive pulmonary disease [76]. Gastric cancer is one of the most common cancers. About 880,000 new gastric cancer patients are diagnosed each year. Gastric cancer is estimated to account for about 10% of invasive cancers worldwide. Wu et al. observed m/z peaks of four proteins (1472.78 Da, 2936.49 Da, 6556.81 Da and 7081.17 Da) in 23 gastric cancer patients compared with 18 controls by MALDI-TOF-MS [16]. In addition, an important discovery is that A1AT, cystatin C, alpha-2-macroglobulin, and transthyretin were found as potential biomarkers (p b 0.01) toward type-2 diabetes by Rao [63]. Pancreatic cancer is one of the four or five most common causes of cancer mortality in developed countries. It leads to an estimated 227000 deaths worldwide each year [46]. New strategies and biomarkers for early pancreatic cancer detection are urgently needed. Researchers from UCLA identified four biomarkers (KRAS, MBD3L2, ACRV1 and DPM1) could differentiate pancreatic cancer patients from non-cancer subjects with 90.0% sensitivity and 95.0% specificity [82]. Wong, D. found that combination of the four biomarkers ACRV1, DMXL2, DPM1 and Streptococcus mitis in saliva could differentiate pancreatic cancer patients from healthy controls with 92.9% sensitivity and 85.5% specificity [69].

C

T

Periodontitis, a chronic disease of the oral cavity, is initiated by a pathogenic bacterial biofilm both at and below the gingival margin. Re461 cent research shows that mild forms of periodontitis, periodontitis of 462 moderate severity, and advanced periodontitis account for 31%, 13% 463 and 4% of the US population, respectively. The combination of ALT 464 Q19 level and the Porphyromonas gingivalis ratio showed significant predic465 tors of the progression of periodontitis (p b 0.001, sensitivity: 0.40, 466 specificity: 0.96) [42]. Some saliva biomarkers such as Immunoglobulins 467 (IgA, IgM, IgG), Lysozyme, Mucins, Lactoferrin, Histatin, C-reactive pro468 tein, and Peroxidase, are also released to periodontitis [79]. Among 469 these biomarkers, Immunoglobulins (Ig) are important specific bio470 markers. N. Ghallab et al. found that salivary soluble-CD44 might be 471 considered as a biomarker of periodontal destruction in smokers and 472 nonsmokers (p b 0.001) [80].

E

459 460

R O

O

F

t3:3

5.2. Systematic diseases

474 Q20

485 486

Saliva has been examined for the diagnostics of a number of systemic diseases ranging from breast cancer to graft versus host disease. Breast cancer is the most common malignancy in United States women, accounting for N40,000 deaths each year. It is very important to discover sensitive biomarkers to diagnose breast cancer. C-erbB-2, a most significant biomarker toward breast cancer was found by SELDITOF/MS [75]. The levels of carcinoembryonic antigen (CEA), epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF) in saliva were significantly elevated in breast cancer patients with salivary VEGF and EGF with a sensitivity of 83% and specificity of 74% [81]. These protein levels and the predictive power of each protein individually and in combination in the diagnosis of breast cancer were provided in Table 4. Mean values of other breast cancer-related protein markers

t4:1 t4:2 t4:3

Table 4 Salivary fluid protein levels and the predictive power of each protein individually and in combination in diagnosis of breast cancer.

483 484

t4:4

R

R

O

481 482

Healthy controls VEGF EGF CEA

t4:16

2.1 ± 1.2 2.1 ± 1.3 66.1 ± 27.1

3.7 ± 1.6 3.7 ± 1.7 83.0 ± 31.0

b0.0001 b0.0001 0.0106

Table 5 Mean values of biomarker in saliva for breast cancer (mean ± SD).

ROC curve analysis on the logistic regression models

t4:11 t4:12 t4:13 t4:14 t4:15

p-Value

Wilcoxon test for each salivary protein (mean ± SD) (ng/ml)

t4:5 t4:6 t4:7 t4:8 t4:9 t4:10

Cancer patients

Until now, the majority of saliva proteomics studies have focused on the hunt for disease biomarker used in the prediction of risk for many diseases. It is now clear that the combination of emerging salivary diagnostics and biotechnologies making the aim close. There are many powerful tools to unveil and analyze biomarkers of many diseases, such as 2DE/MS, LC-MS, MALDI-TOF/MS, and SELDI-TOF/MS. According to the different characteristics of salivary protein biomarkers, different methodologies should be used. The high abundance proteins in human saliva will interfere with the determination of low-abundance biomarkers. Therefore, these high abundance proteins, such as salivary amylases,

C

479 480

N

477 478

U

475 476

E

473

VEGF EGF CEA VEGF + EGF

AUC (%)

Sensitivity (%)

Specificity (%)

80 7765 84

74 78 70 83

73 68 56 74

AUC, area under the curve.

489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 Q21 508 509

513 514 515 516 517 Q22 518 519 520

Biomarker

Control subjects

Benign

Carcinoma

t5:3

CA15-3 (U/mg protein) c-erbB-2 (units/mg protein) P53 (pmol/mg protein) Cathespin-D (pmol/mg protein) Epidermal growth factor (fmol/mg protein)

2.27 ± 1.54 – 177.1 ± 61.3 26.29 ± 17.22 1.03 ± 0.69

2.22 ± 1.95 – 180.7 ± 70.78 0.57 ± 13.05 0.37 ± 0.31

5.26 51.3 134.6 34.5 0.92

t5:4 t5:5 t5:6 t5:7 t5:8 t5:9

± ± ± ± ±

4.12 43.96 63.8 27.95 0.8

Please cite this article as: Wang Q, et al, Emerging salivary biomarkers by mass spectrometry, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.08.037

Q. Wang et al. / Clinica Chimica Acta xxx (2014) xxx–xxx

538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559

U

568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585

References

N C O

The authors are grateful to the financial support from National Recruitment Program of Global Experts (NRPGE), the Hundred Talents 564 Program of Sichuan Province (HTPSP), and the Startup Funding of Si565 chuan University for setting up the Research Center of Analytical 566 Q23 Instrumentation. 562 563

567

F

O

536 537

R O

534 535

C

532 533

E

530 531

R

528 529

R

527

P

Acknowledgments

525 526

[1] Humphrey SP, Williamson RT. A review of saliva: normal composition, flow, and function. J Prosthet Dent 2001;85(2):162–9. [2] Defabianis P, Re F. The role of saliva in maintaining oral health. Minerva Stomatol 2003;52:301–8. [3] Xiao H, Wong DT. Proteomics and its applications for biomarker discovery in human saliva. Bioinformation 2011;5(7):294–6. [4] Lee YH, Wong DT. Saliva: an emerging biofluid for early detection of diseases. Am J Dent 2009;22(4):241–8. [5] De Almeida PV, Gregio AM, Machado MA, De Lima AA, Azevedo LR. Saliva composition and functions: a comprehensive review. J Contemp Dent Pract 2008;9(3): 72–80. [6] Pinka R, Simeka J, Vondrakovab J, Faberb E, Michla P, Pazderaa J, et al. Saliva as a diagnostic medium. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2009; 153(2):103–10. [7] Wong DT. Salivary diagnostics powered by nanotechnologies, proteomics and genomics. J Am Dent Assoc 2006;137(3):313–21. [8] Pfaffe T, Cooper-White J, Beyerlein P, Kostner K, Punyadeera C. Diagnostic potential of saliva: current state and future applications. Clin Chem 2011;57(5):675–87.

[9] Wang Q, Gao P, Wang X, Duan Y. Investigation and identification of potential biomarkers in human saliva for the early diagnosis of oral squamous cell carcinoma. Clin Chim Acta 2013;427:79–85. [10] Amado F, Lobo MJC, Domingues P, Duarte JA, Vitorino R. Salivary peptidomics. Expert Rev Proteomics 2010;7(5):709–21. [11] Edgar WM. Saliva and dental health. Clinical implications of saliva: report of a consensus meeting. Br Dent J 1990;169:96–8. [12] Miller CS, Foley JD, Bailey AL, Campell CL, Humphries RL, Christodoulides N, et al. Current developments in salivary diagnostics. Biomarkers 2010;4(1):171–89. [13] Motamayel FA, Davoodi P, Dalband M, Hendi SS. Saliva as a mirror of the body health. Dent J Hamamdan Univ Med Sci 2010;1:1–15. [14] Navazesh M. Methods for collecting saliva. Ann N Y Acad Sci 1993;694:72–7. [15] Fleissig Y, Deutsch O, Reichenberg E, Redlich M, Zaks B, Palmon A, et al. Different proteomic protein patterns in saliva of Sjögren's syndrome patients. Oral Dis 2009; 15(1):61–8. [16] Wu ZZ, Wang JG, Zhang XL. Diagnostic model of saliva protein finger print analysis of patients with gastric cancer. World J Gastroenterol 2009;15(7):865–970. [17] Wu Y, Shu R, Luo LJ, Ge LH, Xie YF. Initial comparison of proteomic profiles of whole unstimulated saliva obtained from generalized aggressive periodontitis patients and healthy control subjects. J Periodontal Res 2009;44(5):636–44. [18] Ohshiro K, Rosenthal DI, Koomen JM, Streckfus CF, Chambers M, Kobayashi R, et al. Pre-analytic saliva processing affect proteomic results and biomarker screening of head and neck squamous carcinoma. Int J Oncol 2007;30(3):743–9. [19] 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. [20] Hirtz C, Chevalier F, Sommerer N, Raingeard I, Bringer J, Rossignol M, et al. Salivary protein profiling in type I diabetes using two-dimensional electrophoresis and mass spectrometry. Clin Proteomics 2006;2(1):117–27. [21] Schipper R, Loof A, De Groot J, Harthoorn L, Van Heerde W, Dransfield E. Salivary protein/peptide profiling with SELDI-TOF-MS. Ann N Y Acad Sci 2007;1098(1): 498–503. [22] Fang X, Zhang WW. Affinity separation and enrichment methods in proteomic analysis. J Proteomics 2008;71(3):284–303. [23] 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):4150–7. [24] Ehsan S, Ball G, Choke E, Molyneux K, London N, Herbert K, et al. Disease specific biomarkers of abdominal aortic aneurysms detected by Surface Enhanced Laser Desorption Ionization Time of Flight Mass Spectrometry. Eur J Vasc Endovasc 2012;44(1): 52–4. [25] O'Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem 1975;250(10):4007–21. [26] Ghafouri B, Tagesson C, Lindahl M. Mapping of proteins in human saliva using twodimensional gel electrophoresis and peptide mass fingerprinting. Proteomics 2003; 3(6):1003–15. [27] 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(6):1714–28. [28] Yamada N, Yuji R, Suzuki E. The current status and future prospects of the salivary proteome. J Health Sci 2009;55(5):682–8. [29] Petersen A. Two-dimensional electrophoresis replica blotting: a valuable technique for the immunological and biochemical characterization of single components of complex extracts. Proteomics 2003;3(7):1206–14. [30] Hu S, Arellano M, Boontheung P, Wang J, Zhou H, Jiang J, et al. Salivary proteomics for oral cancer biomarker discovery. Clin Cancer Res 2008;14(19):6246–52. [31] van Wietmarschen HA, Dai W, van der Kooij AJ, Reijmers TH, Schroën Y, Wang M, et al. Characterization of rheumatoid arthritis subtypes using symptom profiles, clinical chemistry and metabolomics measurements. PLoS One 2012;7(9):e44331. [32] Tan Y, Yin P, Tang L, Xing W, Huang Q, Cao D, et al. Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol Cell Proteomics 2012;11(2). [33] Ambatipudi KS, Swatkoski S, Moresco JJ, Tu PG, Coca A, Anolik JH, et al. Quantitative proteomics of parotid saliva in primary Sjögren's syndrome. Proteomics 2012. [34] Chen EI, Yates III JR. Cancer proteomics by quantitative shotgun proteomics. Mol Oncol 2007;1(2):144–59. [35] Ye M, Jiang X, Feng S, Tian R, Zou H. Advances in chromatographic techniques and methods in shotgun proteome analysis. Trends Anal Chem 2007;26(1):80–4. [36] Adkins JN, Varnum SM, Auberry KJ, Moore RJ, Angell NH, Smith RD, et al. Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry. Mol Cell Proteomics 2002;1:947–55. [37] Xiao H, Zhang L, Zhou H, Lee JM, Garon EB, Wong DTW. Proteomic analysis of human saliva from lung cancer patients using two-dimensional difference gel electrophoresis and mass spectrometry. Mol Cell Proteomics 2012;11(2). [38] Korostoff A, Reder L, Masood R, Sinha UK. The role of salivary cytokine biomarkers in tongue cancer invasion and mortality. Oral Oncol 2011;47(4):282–7. [39] Preza D, Thiede B, Olsen I, Grinde B. The proteome of the human parotid gland secretion in elderly with and without root caries. Acta Odontol Scand 2009;67(3):161–9. [40] Streckfus CF, Mayorga-Wark O, Arreola D, Edwards C, Bigler L, Dubinsky WP. Breast cancer related proteins are present in saliva and are modulated secondary to ductal carcinoma in situ of the breast. Cancer Invest 2008;26(2):159–67. [41] Tiziana C, Barbara M, Federica I, Chiara F, Sonia N, Antonella F, et al. RP-HPLC–ESI-MS evidenced that salivary cystatin B is detectable in adult human whole saliva mostly as S-modified derivatives: S-glutathionyl, S-cysteinyl and S–S 2-mer. J Proteomics 2011;75:908–13.

D

561

523 524

T

560

should be depleted before analysis. With an increased number of identified proteins expected in the near future, it is the time to devote more attention to the comprehension of their function and the investigation of their contents. With the fast development of mass spectrometry and proteomic technologies, saliva is a growing area for basic and clinical research with substantial potential for disease diagnosis. As a clinical tool, saliva has many advantages over blood, such as collection is non-invasive, inexpensive, and easy-to-use. For patients, saliva collection may diminish discomfort compared with blood collection, particularly for repeated sampling and for elders and children. It is beneficial to the development of home-based saliva tests. Therefore, saliva proteomics holds great promise to improve our understanding as well as identify more and more biomarkers for many kinds of diseases. Saliva proteomics also provide more opportunities for the development of biomarkers for diseases. However, there are still some bottlenecks for salivary proteomic biomarker discovery. Firstly, concentrations of saliva biomarker are found in lower levels in comparison with blood. Secondly, low-abundance proteins will likely prove to be the best biomarkers for early diagnostics of diseases. However, there are some high-abundance proteins in saliva, such as amylase, albumin, immunoglobulin etc., which have to be depleted prior to definitive analyses because they are the hurdles for the detection and quantization of low abundant proteins. Elimination of high-abundance proteins in saliva also can improve the resolution and sensitivity. The other roadblock is that the salivary diagnostics is to develop, validate biomarkers and successfully translate these biomarkers or biomarker panels correlated with diseases from the laboratory level into the clinic practice [4]. The research for the mechanism of saliva biomarkers is needed. In order to accomplish this aim, there are still more work to do. Another challenge in making salivary diagnostics into a clinical reality is to position it as a highly accurate technology that can achieve definitive point-of-care assessment of health and disease status [7]. Although there are some technical and mechanistic challenges in salivary proteome analysis, we are enthusiastic that saliva biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future. In turn, the demands for these technologies will impetus for the salivary proteomics development. With the further development of more sensitive detection technologies, saliva-based diagnostics are being widely accepted by clinicians and patients.

E

521 522

7

Please cite this article as: Wang Q, et al, Emerging salivary biomarkers by mass spectrometry, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.08.037

586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 Q24 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671

D

P

R O

O

F

[62] Magni F, Van Der Burgt YEM, Chinello C, Mainini V, Gianazza E, Squeo V, et al. Biomarkers discovery by peptide and protein profiling in biological fluids based on functionalized magnetic beads purification and mass spectrometry. Blood Transfus 2010;8(Suppl. 3):s92–7. [63] Rao PV, Reddy AP, Lu X, Dasari S, Krishnaprasad A, Biggs E, et al. Proteomic identification of salivary biomarkers of type-2 diabetes. J Proteome Res 2009;8(1):239–45. [64] Streckfus C, Bigler L, Dellinger T, Kuhn M, Chouinard N, Dai X. The expression of the c-erbB-2 receptor protein in glandular salivary secretions. J Oral Pathol Med 2004; 33:595–600. [65] Kondo T. Tissue proteomics for cancer biomarker development: laser microdissection and 2D-DIGE. J Biochem Mol Biol 2008;41(9):626–34. [66] Zhu W, Smith JW, Huang CM. Mass spectrometry-based label-free quantitative proteomics. J Biomed Biotechnol 2010;2010:1–6. [67] Wu WW, Wang G, Baek SJ, Shen RF. Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, using 2D gel-or LC-MALDI TOF/TOF. J Proteome Res 2006;5(3):651–8. [68] Krizkova S, Zitka O, Adam V, Kizek R, Masarik M, Stiborova M, et al. Assays for determination of matrix metalloproteinases and their activity. Trends Anal Chem 2011; 30(11):1819–32. [69] Wong D, Zhang L, Farrell J, Zhou H, Elashoff D, Gao K, et al. Salivary biomarkers for pancreatic cancer detection. J Clin Oncol 2009;27:4630. [70] De Jong EP, Xie H, Onsongo G, Stone MD, Chen XB, Kooren JA, et al. Quantitative proteomics reveals myosin and actin as promising saliva biomarkers for distinguishing pre-malignant and malignant oral lesions. Public Libr Sci 2010;5(6):e11148. [71] Patel VJ, Thalassinos K, Slade SE, Connolly JB, Crombie A, Murrell JC, et al. A comparison of labeling and label-free mass spectrometry-based proteomics approaches. J Proteome Res 2009;8(7):3752–9. [72] Chelius D, Bondarenko PV. Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. J Proteome Res 2002;1(4): 317–23. [73] Liu H, Sadygov RG, Yates JR. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 2004;76(14):4193–201. [74] Rudney JD, Staikov RK, Johnson JD. Potential biomarkers of human salivary function: a modified proteomic approach. Arch Oral Biol 2009;54(1):91–100. [75] Streckfus CF, Bigler LR, Zwick M. The use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to detect putative breast cancer markers in saliva: a feasibility study. J Oral Pathol Med 2006;35:292–300. [76] Nicholas BL, Skipp P, Barton S, Singh D, Bagmane D, Mould R, et al. Identification of lipocalin and apolipoprotein A1 as biomarkers of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2010;181(10):1049–60. [77] Yang LL, Liu XQ, Liu W, Cheng B, Li MT. Comparative analysis of whole saliva proteomes for the screening of biomarkers for oral lichen planus. Inflamm Res 2006;55(10):405–7. [78] Delaleu N, Immervoll H, Cornelius J, Jonsson R. Biomarker profiles in serum and saliva of experimental Sjögren's syndrome: associations with specific autoimmune manifestations. Arthritis Res Ther 2008;10(1):R22. [79] Giannobile WV, Beikler T, Kinney JS, Ramseier CA, Morelli T, Wong DT. Saliva as a diagnostic tool for periodontal disease: current state and future directions. Periodontology 2000 2009;50(1):52–64. [80] Ghallab N, Shaker O. Salivary-soluble CD44 levels in smokers and non-smokers with chronic periodontitis: a pilot study. J Periodontol 2010;81(5):710–7. [81] Brooks MAIN, Wang J, Li Y, Zhang R, Elashoff D, Wong DT. Salivary protein factors are elevated in breast cancer patients. Mol Med Rep 2008;1(3):375–8. [82] Zhang L, Farrell JJ, Zhou H, Elashoff D, Akin D, Park NH, et al. Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer. Gastroenterology 2010; 138(3):949–57.

N

C

O

R

R

E

C

T

[42] Nomura Y, Shimada Y, Hanada N, Numabe Y, Kamoi K, Sato T, et al. Salivary biomarkers for predicting the progression of chronic periodontitis. Arch Oral Biol 2012. [43] Millea KM, Krull IS, Chakraborty AB, Gebler JC, Berger SJ. Comparative profiling of human saliva by intact protein LC/ESI-TOF mass spectrometry. BBA Proteins Proteomics 2007;1774(7):897–906. [44] Kataoka H, Inoue R, Yagi K, Saito K. Determination of nicotine, cotinine, and related alkaloids in human urine and saliva by automated in-tube solid-phase microextraction coupled with liquid chromatography–mass spectrometry. J Pharm Biomed Anal 2009; 49(1):108–14. [45] Zenobi R, Knochenmuss R. Ion formation in MALDI mass spectrometry. Mass Spectrom Rev 1998;17(5):337–66. [46] Vincent A, Herman J, Schulick R, Hruban RH, Goggins M. Pancreatic cancer. Lancet 2011;378(9791):607–20. [47] Hu S, Yu T, Xie Y, Yang Y, Li Y, Zhou X, et al. Discovery of oral fluid biomarkers for human oral cancer by mass spectrometry. Cancer Genomics Proteomics 2007; 4(2):55–64. [48] Jou YJ, Lin CD, Lai CH, Chen CH, Kao JY, Chen SY, et al. Proteomic identification of salivary transferrin as a biomarker for early detection of oral cancer. Anal Chim Acta 2010;681(1–2):41–8. [49] Jou YJ, Lin CD, Lai CH, Tang CH, Huang SH, Tsai MH, et al. Salivary zinc finger protein 510 peptide as a novel biomarker for detection of oral squamous cell carcinoma in early stages. Clin Chim Acta 2011;412(15–16):1357–65. [50] Terracciano R, Preianò M, Palladino GP, Carpagnano GE, Barbaro MPF, Pelaia G, et al. Peptidome profiling of induced sputum by mesoporous silica beads and MALDI-TOF MS for non-invasive biomarker discovery of chronic inflammatory lung diseases. Proteomics 2011;11(16):3402–14. [51] Hu S, Wang J, Meijer J, Ieong S, Xie Y, Yu T, et al. Salivary proteomic and genomic biomarkers for primary Sjögren's syndrome. Arthritis Rheum 2007;56(11):3588–600. [52] Imanguli MM, Atkinson JC, Harvey KE, Hoehn GT, Ryu OH, Wu T, et al. Changes in salivary proteome following allogeneic hematopoietic stem cell transplantation. Exp Hematol 2007;35(2):184–92. [53] Huang CM, Zhu W. Profiling human saliva endogenous peptidome via a high throughput MALDI-TOF–TOF mass spectrometry. Comb Chem High Throughput Screen 2009;12(5):521–31. [54] Popescu ID, Albulescu R, Raducan E, Dinischiotu A, Tanase C. Applications of SELDITOF technology in cancer biomarkers discovery. Rom Biotechnol Lett 2010;15: 5654–67. [55] Chibo L. The application of SELDI-TOF-MS in clinical diagnosis of cancers. J Biomed Biotechnol 2011;2011:1–6. [56] Shintani S, Hamakawa H, Ueyama Y, Hatori M, Toyoshima T. Identification of a truncated cystatin SA-I as a saliva biomarker for oral squamous cell carcinoma using the SELDI ProteinChip platform. Int J Oral Maxillofac Surg 2010;39(1):68–74. [57] Streckfus CF, Bigler LR, Zwick M. The use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to detect putative breast cancer markers in saliva: a feasibility study. J Oral Pathol Med 2006;35:292–300. [58] Ryu OH. Identification of parotid salivary biomarkers in Sjogren's syndrome by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry and two-dimensional difference gel electrophoresis. Rheumatology 2006;45(9): 1077–86. [59] Schipper R, Loof A, De Groot J, Harthoorn L, Dransfield E, Van Heerde W. SELDI-TOFMS of saliva: methodology and pre-treatment effects. J Chromatogr B 2007;847(1): 45–53. [60] Papale M, Pedicillo MC, Di Paolo S, Thatcher BJ, Lo Muzio L, Bufo P, et al. Saliva analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF/MS): from sample collection to data analysis. Clin Chem Lab Med 2008;46(1):89–99. [61] Silberring J, Ciborowski P. Biomarker discovery and clinical proteomics. Trends Anal Chem 2010;29(2):128–40.

U

672 673 Q25 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 Q26 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 786

Q. Wang et al. / Clinica Chimica Acta xxx (2014) xxx–xxx

E

8

Please cite this article as: Wang Q, et al, Emerging salivary biomarkers by mass spectrometry, Clin Chim Acta (2014), http://dx.doi.org/10.1016/ j.cca.2014.08.037

730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785