Skip to content
BY 4.0 license Open Access Published by De Gruyter Open Access October 6, 2022

Influence of different sample preparation strategies on hypothesis-driven shotgun proteomic analysis of human saliva

  • Vladimíra Schwartzová , Ivan Talian EMAIL logo and Galina Laputková
From the journal Open Chemistry

Abstract

This research aimed to find an efficient and repeatable bottom-up proteolytic strategy to process the unstimulated human saliva. The focus is on monitoring immune system activation via the cytokine and interleukin signaling pathways. Carbohydrate metabolism is also being studied as a possible trigger of inflammation and joint damage in the context of the diagnostic procedure of temporomandibular joint disorder. The preparation of clean peptide mixtures for liquid chromatography–mass spectrometry analysis was performed considering different aspects of sample preparation: the filter-aided sample preparation (FASP) with different loadings of salivary proteins, the unfractionated saliva, amylase-depleted, and amylase-enriched salivary fractions. To optimize the efficiency of the FASP method, the protocols with the digestion in the presence of 80% acetonitrile and one-step digestion in the presence of 80% acetonitrile were used, omitting protein reduction and alkylation. The digestion procedures were repeated in the standard in-solution mode. Alternatively, the temperature of 24 and 37°C was examined during the trypsin digestion. DyNet analysis of the hierarchical networks of Gene Ontology terms corresponding to each sample preparation method for the bottom-up assay revealed the wide variability in protein properties. The method can easily be tailored to the specific samples and groups of proteins to be examined.

1 Introduction

Human saliva refers to the mucoserous exocrine complex biological fluid composed of secretions from the major (submandibular, parotid, and sublingual) salivary glands as well as the abundance of smaller salivary glands distributed throughout the oral mucosa [1].

Saliva offers an attractive alternative to traditional blood collection methods as the collection is non-invasive, simple, and more acceptable for repeat testing [2].

Saliva has emerged as a promising tool with extensive diagnostic potential in various biomedical fields, including medicine and dentistry, mainly due to recent advances in mass spectrometry-based proteomics techniques [3].

Salivaomics technology enabled the translation of the diagnostic potential of saliva into clinical practice. The salivary proteome can offer potent markers for both local and systemic diseases ranging from Sjögren’s syndrome to metabolic diseases and cancer [4,5,6]. In addition, changes in salivary protein composition provide insight into disease etiology and the molecular mechanisms underlying disease pathology.

The lack of standardized processes and analytical data continue to hamper saliva analysis as a common strategy in a clinical environment. The sampling of saliva with different swab-based devices such as Super Sal™ (Oasis Diagnostic Corporation), Versi Sal™ (Oasis Diagnostic Corporation), Quantisal® (Immunalysis Corporation); Intercept® (OraSure Technologies Inc.), Salivette® (blue cap, Sarstedt), SOS (Salimetrics), Toothette-Plus swabs (Sage Products Inc.), OraQuick Advance HIV-1/2 (OraSure Technologies Inc.), and BBL CultureSwab orange and white cap could contribute to the improvement of this process [7].

Saliva is one of the biological fluids of choice in temporomandibular joint (TMJ) pathology biomarker research [8,9,10]. In this context, increasing attention is being paid to the study of macrophage-derived inflammatory/anti-inflammatory cytokines, e.g., tumor necrosis factor, interleukin (IL)-1 beta, IL-6, IL-8, IL-10, and IL-12 and related host inflammatory mediators to provide information about the inflammatory process of TMJ [11,12]. Inflammation is closely related to abnormal glucose and lipid metabolism. Dysregulated glucose metabolism is the driving force behind cartilage growth abnormalities as glucose is essential for the maintenance of chondrocyte metabolism and is a precursor to key matrix components [13]. However, the disturbance and influence of the glycolytic metabolism on the TMJ remains unclear.

The experimental design considered for a successful proteomics experiment relies on a bottom-up strategy in terms of characterizing proteins that undergo enzymatic cleavage into smaller peptide fragments. In a standard shotgun proteomics workflow, the peptide mixture is fractionated and subjected to liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) [14].

The efficiency and reproducibility of sample preparation for MS-based proteomics is a central issue for ensuring high-quality data. The key steps in preparing proteomic samples include: (1) extraction, solubilization, and denaturation to attain unfolded proteins; (2) proteolytic digestion; (3) detergent removal and desalting; and (4) fractionation [15].

Over the years, several methods of sample preparation for MS-based proteomics have been introduced. Traditional proteomics approaches based on two-dimensional gel electrophoresis seem to be slow and tedious [16,17]. Instead, several effective strategies for proteomic studies have been employed, which have been further developed and modified.

Proteolytic cleavage in solution represents simultaneous digestion of many proteins, in contrast to in-gel proteolysis where tryptic enzymes act on few proteins [18]. Although the technique of cleavage in solution is often ineffective in resolving all transmembrane proteins or proteins with numerous post-translational modifications, their various modifications have proven useful [19]. This process always requires subsequent desalting and concentration of proteolytic products.

Recently, a one-step procedure was developed that allows the conversion of a protein mixture to the tryptic digest in a single tube in less time [20,21]. Regarding organic solvents, aqueous acetonitrile (ACN) solvent systems have been extensively tested by several research teams to increase trypsin digestion efficiency and maximize the number of identified proteins [22,23,24].

Filter-aided sample preparation (FASP) was developed to overcome the drawbacks associated with detergents and peptide purification [25,26]. Sample preparation is performed on ultrafiltration spin instruments using a molecular weight cut-off membrane (MWCO). In the original FASP method, sodium dodecyl sulfate detergent is first used to solubilize proteins, and then they are denatured with urea and then digested [26]. Recently, the FASP protocol was modified to optimize the proteolysis and efficiency of the procedure [27,28,29].

The shortcomings of the standard sample preparation techniques have been refined by a number of innovative sample preparation approaches. These innovative technologies exhibit miniaturization, which implies that both their sample volume and the amount of an extracting phase are significantly smaller than those of conventional methods. One of the most effective methods for sample preparation is solid phase micro-extraction (SPME), which may be used to extract and enrich analytes from complex matrices [30].

The SPME variation known as in-tube solid-phase microextraction employs an open-tubular capillary column as the extraction unit. It is typically combined with online column switching technology and high-performance liquid chromatography (HPLC), where the whole procedure − from sample processing through separation to data analysis − is automated with the use of an autosampler [31,32].

The modification of SPME in thin film geometry in the coated blade and membrane arrangements with a single extraction phase providing robust and convenient in vivo sampling demonstrated faster analysis and higher extraction recovery in saliva extraction and separation and the applicability of the method for the SPME-LC-MS/MS analysis [33].

Development of in vivo SPME swab integrates an SPME fiber and a medical swab [34]. In order to combine with various mass spectrometry methods for multidimensional study of human saliva, the multiple SPME fibers incorporated into a medical swab (multiple-SPME swab) were developed [35].

Recently, solid-phase-enhanced sample-preparation (SP3) technology was created to make it easier to examine a wide variety of sample types, including simple as well as complex protein mixtures in different amounts, across many different species. It involves a paramagnetic bead-based technique and takes only about 30 min to process protein samples for proteome analysis in a quick, reliable, and efficient manner. Before performing a downstream proteomic analysis, SP3 exchanges or removes substances (such as detergents, chaotropes, salts, buffers, acids, and solvents) that are frequently employed to assist cell or tissue lysis, protein solubilization, and enzymatic digestion [36].

Study of the salivary proteome is challenging due to the numerous highly expressed proteins such as amylase, mucin, and albumin. Therefore, specialized approaches are required to remove them (particularly amylase) from saliva to increase the detection limit and identify low-abundance proteins [37].

Since efficient and reliable proteolytic digestion of the samples is crucial for the outcome of the bottom-up proteomics analysis, the aim of this study was to evaluate the performance of several different protocols adopted from the literature on selected human salivary proteins. According to the literature, these proteins can be used as diagnostic or prognostic indicators. This work monitors the effectiveness and repeatability of sample preparation methods on the number of identified proteins from selected signaling pathways and the composition of the identified proteins in relation to their functionality.

2 Materials and methods

2.1 Reagents

Dithiothreitol (DTT), iodoacetamide (IAA), and urea were purchased from Bio-Rad Laboratories, Hercules, CA, USA. Acetone and ethanol were purchased from Merck, Darmstadt, Germany. Formic acid (FA) and ammonium bicarbonate (AB) were purchased from AppliChem GmbH, Darmstadt, Germany. Thiourea and potato starch were manufactured by Sigma-Aldrich, St. Louis, MO, USA. Trifluoroacetic acid (TFA) was purchased from Acros Organics B.V.B.A., Fisher Scientific, Belgium, while ACN was purchased from Honeywell Fluka, Charlotte, North Carolina, USA. Sequencing grade modified trypsin was obtained from Promega, Madison, Wisconsin, USA. All ultra-centrifugal filters: Amicon Centrifugal Filter Unit Ultra-4, MWCO 50 kDa; Amicon Centrifugal Filter Units Ultra-0.5 mL, MWCO 30 kDa; and Microcon 0.5 mL centrifugal filter units, MWCO 30 kDa were purchased from Merck Millipore, Billerica, MA, USA. Solid phase extraction (SPE) cartridges Bond Elut C 18 50 mg mL−1 were obtained from Agilent Technologies, Santa Clara, CA, USA. For Coomassie dye-binding assays, Quick Start Bradford Protein Assay from Bio-Rad, Hercules, California, USA was used.

2.2 Patients, sampling, and handling procedures

All unstimulated whole saliva samples were obtained from a single healthy donor to reduce sample variability. The participant was instructed to abstain from eating and oral hygiene procedures 2 h prior to saliva collection. To avoid the interference from the circadian cycle, saliva samples were collected between 9 and 11 a.m. [38].

Saliva collection was performed as previously described by spitting into sterile 50 mL Falcon tubes kept on ice [39]. Approximately 3 mL of saliva was accumulated over 5–10 min. Then, the samples were centrifuged at 12,000×g for 30 min at 4°C to remove debris from insoluble material and cell fragments. The supernatant of each sample was aspirated and stored at −80°C until further use.

  1. Informed consent: All experiments were conducted in accordance with accepted ethical principles in medical research involving humans, including the World Medical Association Declaration of Helsinki. The participant provided a signed informed consent form.

  2. Ethical approval: The study protocol was approved by the Ethics Committee of the Louis Pasteur University Hospital in Košice (Approval 2020/EK/06046).

2.3 Sample preparation for proteome analysis

2.3.1 Protocol A

The saliva sample from a healthy individual was divided into two parts. 1 mL remained unchanged, while about 1.2 mL of saliva was used to prepare fraction 1 < 50 kDa and fraction 2 > 50 kDa. For this, saliva was filtered using an Amicon centrifugal filter unit Ultra-4, MWCO 50 kDa at 7,000×g for 15 min at 4°C.

Proteins of the whole unstimulated saliva and from both fractions 1 and 2 were precipitated by mixing the samples with acetone supplemented with 0.2% DTT in 1:5 (v/v) ratios. After overnight incubation at −25°C, proteins were pelleted by centrifugation at 12,000×g for 30 min at 4°C. The pelleted proteins were washed twice with ice-cold glacial acetone (−25°C), vortexed and pelleted again by centrifugation at 12,000×g for 20 min at 4°C and air-dried at room temperature.

The protein pellets were resuspended in 50–100 µL of 8 mol L−1 urea/2 mol L−1 thiourea/400 mmol L−1 AB. A volume of about 50–100 µL containing 100 µg of salivary proteins was used for further treatment.

The proteins were reduced by the addition of 100 mmol L−1 DTT dissolved in 50 mmol L−1 AB to 5 mmol L−1 final concentration of DTT and kept at 50°C for 60 min. The sample was then alkylated with 100 mmol L−1 IAA in 50 mmol L−1 AB to a final concentration of 12.5 mmol L−1 IAA and kept in a thermoshaker for 30 min at room temperature in the dark. The reaction was quenched by the addition of the same volume of DTT as before. The solution was diluted with 50 mmol L−1 AB to a final concentration of 1 mol L−1 urea, 250 mmol L−1 thiourea, and 50 mmol L−1 AB. A standard overnight enzymatic digestion was performed in a thermoshaker at 37°C by the addition of trypsin dissolved in ice-cold 50 mmol L−1 AB at an enzyme-to-protein ratio of 1:50 (w/w). To inactivate trypsin, the solution was acidified by the addition of 10% FA (formic acid:H2O, 90:10, v/v) to a final concentration of 0.1%.

The next step was to remove salts, ampholytes, and other potentially interfering substances using Bond Elut C 18 SPE cartridges. The peptides were eluted into 1 mL of 70% ACN, 0.1% FA (ACN:H2O:FA (70:29.9:0.1, v/v/v)) and evaporated to 40 µL with a vacuum evaporator.

2.3.2 Protocol B

The saliva sample from a healthy volunteer was divided into two parts. 1 mL of the whole unstimulated saliva served as a control; 2 mL of saliva was used to prepare an amylase-depleted fraction. The in-house affinity chromatography method according to refs [40,41] was used for this. The system consists of a 1 mL plastic syringe with a 0.45 µm filter unit. The syringe was packed with 700 μg of starch previously washed 3 times with deionized water. A 1 mL saliva sample was applied to the starch column and slowly forced through the syringe by hand. The amylase-depleted filtrate of 2 mL of saliva was pooled. Solution enriched with amylase and its complex partners was eluted using 1 mL of 0.005% hydrochloric acid. Fractions enriched in amylase were also pooled. Both fractions were centrifuged at 12,000×g for 10 min to remove insoluble material.

Salivary proteins from unfractionated saliva, the amylase-depleted, and the amylase-enriched fractions were precipitated by mixing the samples with acetone supplemented with 0.2% DTT in 1:5 (v/v) ratios. Then, the further steps were identical to the procedure of protocol A.

2.3.3 Protocol C

For filter-added sample preparation, Amicon Ultra-0.5 mL MWCO 30 kDa centrifugal filter units were used. The centrifugal filter was rinsed with 100 μL of 70% ethanol and centrifuged at 14,000×g for 30 min at 20°C to remove traces of glycerol before use. The filter was then loaded with 10, 25, 50, or 100 μg of salivary proteins (10, 25, 50, or 100 μL of saliva) and then centrifuged at 14,000×g for 10 min at 20°C until the filter was dry and there was no more saliva on the top of the membrane.

Thereafter, the proteins present in the membrane were washed with 200 μL of 8 mol L−1 urea in 25 mmol L−1 AB to remove low-weight material and then centrifuged at 14,000×g for 20 min at 20°C.

To reduce proteins, 200 μL of 50 mmol L−1 DTT in 8 mol L−1 urea and 25 mmol L−1 AB was added and incubated for 60 min at 37°C and then centrifuged at 14,000 g for 15 min at 20°C. Protein alkylation of cysteine residues was performed by the addition of 100 μL 50 mmol L−1 IAA in 8 mol L−1 urea and 25 mmol L−1 AB solution. After incubation in the dark at 37°C for 45 min, the sample was centrifuged at 14,000×g for 20 min at 20°C.

Afterwards, the filters were washed twice with 200 μL of 25 mmol L−1 AB followed by centrifugation of the filters at 14,000×g, at 20°C for 5 min each time.

Proteolysis was performed by addition of proteomic grade trypsin dissolved in 12.5 mmol L−1 AB at an enzyme-to-protein ratio of 1:30 (w/w). Proteins were digested overnight at 37°C in a thermoshaker. The digested peptides were collected in a clean centrifuge tube by centrifugation at 14,000×g, at 20°C for 15 min, followed by two additional 200 μL washes with deionized water at 14,000×g, at 20°C for 5 min and 16 min, respectively, and then vacuum dried at 55°C. The peptides were resuspended in 50 μL of 3% (v/v) ACN containing 0.1% (v/v) aqueous FA. The samples were then homogenized for 5 min on a vortex, followed by 10 min in an ultrasonic bath at 100% ultrasonic amplitude.

2.3.4 Protocol D1

Amicon Ultra-0.5 mL MWCO 30 kDa centrifugal filter unit was replaced with Microcon 0.5 mL MWCO 30 kDa centrifugal filter unit.

The filter was loaded with a saliva volume of 25 or 50 μg proteins and then centrifuged at 14,000×g, at 20°C until the filter was dry and no saliva was present on the top of the membrane. Then, the further steps were identical to the procedure of protocol C.

2.3.5 Protocol D2

The protocol is identical to protocol D1. Both the saliva and the amylase-depleted fraction were examined.

2.3.6 Protocol D3

The protocol is identical to protocol D1. Saliva was digested at 24 and 37°C.

2.3.7 Protocol E

Microcon 0.5 mL centrifugal filter units with MWCO of 30 kDa were rinsed with 100 μL of deionized water and centrifuged at 14,000×g for 25 min at 20°C. The filters were loaded with 50 μg of salivary proteins and then centrifuged at 14,000×g, at 20°C until the filter was dry.

After that, the proteins present in the membrane were washed with 200 μL of 80% ACN in 25 mmol L−1 AB solution. The samples were homogenized for 5 min on an ultrasonic bath at 100% amplitude and then centrifuged for 8 min at 14,000×g, at 20°C.

To reduce proteins, 200 μL of 50 mmol L−1 DTT in 80% ACN and 25 mmol L−1 AB was added and incubated for 60 min at 20°C and then centrifuged at 14,000×g, for 16 min at 20°C. Protein alkylation of cysteine residues was performed by the addition of 100 μL of 50 mmol L−1 IAA in 80% ACN and 25 mmol L−1 AB solution. After incubation in the dark at room temperature for 45 min, the sample was centrifuged at 14,000×g for 3 min at 20°C.

Thereafter, the filters were washed twice with 200 μL of 25 mmol L−1 AB, followed by centrifugation of the filters at 14,000×g, at 20°C for 8 min and 13 min.

The enzymatic digestion was performed by addition of proteomic grade trypsin dissolved in 80% ACN and 25 mmol L−1 AB at an enzyme-to-protein ratio of 1:30 (w/w). Proteins were digested alternately at 37 and 24°C in a thermoshaker overnight. The digested peptides were collected in a clean centrifuge tube by centrifugation at 14,000×g, at 20°C for 5 min followed by two additional 200 μL washes with deionized water at 14,000×g, at 20°C for 5 and 10 min, then vacuum dried at 50°C. The peptides were resuspended in 50 μL of 3% (v/v) ACN containing 0.1% (v/v) aqueous FA. The samples were then homogenized for 5 min on a vortex, followed by 10 min in an ultrasonic bath at 100% ultrasonic amplitude.

2.3.8 Protocol F

Saliva was divided into two parts for the filter-added sample preparation on Microcon 0.5 mL MWCO 30 kDa centrifugal filter units. Both the centrifugal filters were rinsed with 100 μL of deionized water and centrifuged at 14,000×g for 25 min at 20°C.

The filters were loaded with 50 μg of salivary proteins and then centrifuged at 14,000×g, at 20°C until the filter was dry and there was no saliva on the top of the filter.

After that, the proteins present in the membrane were washed with 200 μL of 80% ACN in 25 mmol L−1 AB solution. The samples were homogenized for 2 h in an ultrasonic bath at 100% amplitude, and then centrifuged at 14,000×g, for 8 min at 20°C.

Thereafter, the filters were washed twice with 200 μL of 25 mmol L−1 AB followed by centrifugation of the filters at 14,000×g, at 20°C for 8 and 13 min.

The enzymatic digestion was carried out by adding proteomic grade trypsin dissolved in 80% ACN in 25 mmol L−1 AB at an enzyme-to-protein ratio of 1:30 (w/w). Proteins were digested overnight at 37 or 24°C in a thermoshaker. The digested peptides were collected in a clean centrifuge tube by centrifugation at 14,000×g, at 20°C for 5 min followed by 2 additional 200 μL washes with deionized water at 14,000×g, at 20°C for 5 and 10 min, then vacuum dried at 50°C. The peptides were resuspended in 50 μL of 3% (v/v) ACN containing 0.1% (v/v) aqueous FA. The samples were then homogenized for 5 min on a vortex, followed by 10 min in an ultrasonic bath at 100% ultrasonic amplitude.

2.3.9 Protocol G

The saliva volume of 50 μg of proteins was mixed with ACN to a final concentration of 80% ACN.

The sample was homogenized in an ultrasonic bath for 1.5 h at 100% amplitude and then centrifuged at 14,000×g for 30 min at 20°C. The supernatant was used for further analysis.

The enzymatic digestion was performed in solution by the addition of 1.66 μg of proteomic grade trypsin to achieve an enzyme-to-protein ratio of 1:30 (w/w). Proteins were digested for 1 h at 37 or 24°C in a thermoshaker (750 rpm). The digested peptides were vacuum dried at 50°C to remove ACN. The peptides were resuspended in 50 μL of 3% (v/v) ACN containing 0.1% (v/v) aqueous FA. The samples were then homogenized for 5 min on a vortex, followed by 10 min in an ultrasonic bath at 100% ultrasonic amplitude.

The samples for each procedure were prepared as biological duplicates.

2.4 Total protein concentration assay

A Coomassie assay using bovine gamma albumin standards was used to determine total protein content in the saliva samples. The absorbance was measured at 595 nm using a UV-VIS spectrophotometer (UV-3600 Spectrophotometer, Shimadzu Corp., Kyoto, Japan).

2.5 Sample desalting

Salts, ampholytes, and other potentially interfering substances were removed using Bond Elut C 18 SPE cartridges. Initially, the C 18 column was washed twice with both 1 mL of ACN:H2O (50:50, v/v) and 1 mL of H2O:ACN:TFA (94.5:5:0.5, v/v/v). 1 mL of sample solution was then loaded onto the SPE column. Unbound components were first washed off with H2O:ACN:TFA (94.5:5:0.5, v/v/v). Thereafter, the peptides were eluted with 1 mL ACN:H2O:FA (70:29.9:0.1, v/v/v) into low adhesion tubes. Sample volume was reduced by vacuum concentration (Labconco CentriVap Acid-Resistant Vacuum Concentration System, Labconco, Kansas City, MO, USA).

2.6 HPLC-MS/MS analysis and protein identification

For HPLC-MS analysis, an AmaZon Speed ETD ion trap mass spectrometer (Bruker Daltonik, Bremen, Germany) coupled to an Ultimate 3000 RSLC NCP system (Thermo Scientific, USA) was used. 2 µL of each sample was injected into an Acclaim PepMap 100 (Dionex, Thermo Scientific, USA) trap column, 100 µm × 2 cm, C 18, 5 µm particles with H2O:ACN loading solvent at a ratio of 2:98 (v/v) containing 0.1% FA at a flow rate of 8 µL min−1. The peptides were eluted and separated on a home-made 75 µm × 30 cm capillary column packed with reverse phase C 18, 3 µm particles (Magic C 188 AQ, Michrom Bioresources, USA). The mobile phases consisted of 0.1% FA in H2O:ACN 98:2 (v/v) ‒ A and 0.1% FA in H2O:ACN 5:95 (v/v) ‒ B operated at a constant flow of 0.4 µL min−1. The gradient is shown in Table 1. All differentially prepared samples were measured in duplicates in auto MS/MS mode, 10 precursors for 1 MS scan; only 2+ and 3+ precursors were taken for fragmentation with an active exclusion set to 0.5 min. The ICC target was set to 400,000 for MS and MS/MS scan, the maximum accumulation time was 0.050 s for MS and 0.1 s for MS/MS. The isolation window was set to 2.2 Da and the scan range was 300–1,300 Da. The search engine Mascot 2.4 (Bruker Daltonik, Bremen, Germany) against the Swiss-Prot database was used to identify the proteins. The parameters were determined as follows: taxonomy – Homo sapiens (human), variable modification – oxidation of methionine, fixed modification – carbamidomethylation of cysteine, MS tolerance – 0.6 Da, MS/MS tolerance – 0.6 Da, the false discovery rate (FDR) threshold was set to 1%.

Table 1

Gradient profile used for peptide C 188 separation

Time 0 min 5 min 83 min 85 min 100 min 108 min 123 min
B% 4 4 35 95 95 4 4
  1. Ethical approval: The protocol of this study was submitted and approved by the Ethics Committee of the Louis Pasteur University Hospital in Košice (Approval 2020/EK/06046). All experiments were conducted in accordance with the accepted ethical principles in medical research involving humans, including the World Medical Association Declaration of Helsinki. The participant provided a signed informed consent form.

3 Results

Optimizing the saliva sample preparation protocol for bottom-up proteomics represents a significant step towards developing robust and reproducible methods while remaining simple and time-saving.

In this study, the preparation of clean peptide mixtures from saliva for LC-MS analysis was performed according to the different protocols (A, B, C, D1-3, E, F, and G).

The methods were chosen to address different aspects of sample preparation. Two similar centrifugal filters, allowing for the removal of contaminants and detergents were used for FASP with different loadings of salivary proteins, the unfractionated saliva, the amylase-depleted, and the amylase-enriched salivary fractions. To optimize the enzymatic digestion and the efficiency of the FASP procedure, the protocols with digestion in the presence of 80% ACN and one-step digestion in the presence of 80% ACN were used, omitting the steps of protein reduction and alkylation. The digestion procedures were repeated in standard in-solution mode. Alternatively, the temperature of 24 and 37°C of the sample during trypsin digestion was examined.

Table 2 summarizes the saliva sample preparation protocols and provides the number of proteins identified per sample preparation method along with the number of identified proteins in monitored metabolic pathways. Figure 1 shows the correlation between the number of identified proteins in monitored signaling pathways and strategies for preparing saliva samples. Only a protein loading of the filter of 50 µg is shown in the graph as this amount was evaluated as the optimal protein concentration for FASP protocols (10, 25, and 100 µg were also tested).

Table 2

The protocols for preparing the saliva sample with the proteins identified per sample preparation method and signaling pathway monitored

Protocol Type of procedure Details of procedure Number of proteins identified Proteins identified in cytokine signaling pathway R-HSA-1280215 Proteins identified in glucose metabolism signaling pathway R-HSA-70326
Protocol B In-solution Unfractionated saliva 133 9 7
Amylase-depleted fraction 159 14 7
Amylase-enriched fraction 88 6 0
Protocol C FASP/Amicon Load of proteins 10 µg 87 0 4
Load of proteins 25 µg 128 12 7
Load of proteins 50 µg 126 14 7
Load of proteins 100 µg 118 0 7
Protocol D1 FASP/Microcon Load of proteins 25 µg 82 3 2
Load of proteins 50 µg 129 6 3
Protocol D2 FASP/Microcon Unfractionated saliva 159 10 8
Amylase-depleted fraction 135 11 9
Protocol D3 FASP/Microcon Temperature of digestion 37°C 124 13 7
Temperature of digestion 24°C 122 10 7
Protocol E FASP/Microcon Temperature of digestion 37°C, 80% ACN 118 8 6
Temperature of digestion 24°C, 80% ACN 133 12 7
Protocol F FASP/Microcon One-step digestion, temperature of digestion 37°C, 80% ACN 83 8 6
One-step digestion, temperature of digestion 24°C, 80% ACN 59 4 3
Protocol G In-solution One-step digestion, temperature of digestion 37°C, 80% ACN 26 0 0
One-step digestion, temperature of digestion 24°C, 80% ACN 15 0 0
Figure 1 
               Number of identified proteins in monitored signaling pathways in correlation with the sample preparation strategies.
Figure 1

Number of identified proteins in monitored signaling pathways in correlation with the sample preparation strategies.

Protein annotations were mainly obtained from UniProt 7.0 including accession and entry name. The Gene ID Conversion Tool from DAVID Bioinformatics Resources 6.8 [42] was used to convert UniProt entry names to gene IDs. Cytoscape environment (Cytoscape 3.9.0 [43]) for integrated models of biomolecular interaction networks with DyNet (version 1.0.0) [44], Biological Network Gene Ontology (BiNGO) (version 3.0.5) [45], and stringApp (version 1.7.0) [46] plug-ins were used to describe various aspects of functional annotation of networks.

The proteome data were subjected to various functional analysis tools to systematically investigate the proteome profile changes that occurred with individual saliva sample preparation methods. The Cytoscape application DyNet was used for the visualization and comparison of network attributes (nodes or edges) in a pairwise mode [44]. BiNGO plug-in in Cytoscape environment was used for gene ontology term enrichment analysis of three categories, i.e., biological process, molecular function, and cellular component. These annotation clusters facilitated assessment of over-representation or under-representation of GO categories and visualization of the connections associated with different proteins in various categories within GO. The enrichment analysis was performed using a hypergeometric distribution test. GO terms were selected after correction for a multiple term testing with a Benjamini and a Hochberg FDR using a Bonferroni correction at a significance level of p < 0.05.

3.1 Comparison of in-solution strategies

The in-solution sample preparation strategies used in this proteomic study included several different approaches. In the first approach, the human saliva samples were first pre-fractionated through the 50 kDa cut-off filter and both fractions (<50 kDa and >50 kDa) were then digested (protocol A) and analyzed. This approach resulted in poor protein identification, 8 proteins <50 kDa and 29 proteins >50 kDa, and was not investigated further. Another approach, protocol B, splits the saliva sample into two fractions and successfully helps identify less abundant proteins of interest using amylase depletion (Table 2, Figure 2). The in-solution digestion protocol with the so called one-step digestion (protocol G) led to a protein identification that was around 50% worse than the classic in-solution digestion (protocol B), independent of the temperature. The DyNet plug-in was used to compare two network states and highlight differences in specific and common categories corresponding to protein groups identified by classical in-solution digestion and a one-step digestion strategy. The network consisted of 118 functional partners (nodes) and 749 edges (Figure 2).

Figure 2 
                  DyNet visualization of assembly gene/protein networks identified by in-solution digestion strategies: only protocol B ‒ green circles, only protocol G ‒ red circles, and both samples ‒ white circles.
Figure 2

DyNet visualization of assembly gene/protein networks identified by in-solution digestion strategies: only protocol B ‒ green circles, only protocol G ‒ red circles, and both samples ‒ white circles.

The hierarchical networks of GO categories were also explored through the DyNet application in the Cytoscape environment.

Enrichment maps (not shown) were also analyzed to look for variations in samples prepared by the in-solution method assessed by GO molecular function and biological process categories. The 55 and the 19 GO molecular functions were found enriched for networks B and G, respectively. In the top 5 GO molecular function categories with the smallest p-values chosen as significant, peptidase and endopeptidase inhibitor and regulator activities predominated in all methods.

The hierarchical networks revealed the diversity and interdependence of the biological processes related to GO. The 101 and the 37 GO biological process terms were found enriched in methods B and G, respectively. The top 5 GO categories of biological processes with the smallest p-values were chosen as significant (Table 3). As shown in Table 3, for protocol B the processes associated with carbohydrates metabolism were dominant. Glucose catabolic process, hexose catabolic process, monosaccharide catabolic process, alcohol catabolic process, and cellular carbohydrate catabolic process were not included in the list of categories of biological process associated with proteins/genes identified by method G.

Table 3

GO categories of biological processes with the smallest p-values for proteins identified in the saliva sample prepared by the in-solution methods B and G

GO-ID Description GO p-value Corr. p-value
Protocol B
6,007 Glucose catabolic process 2.90 × 10−14 3.64 × 10−11
19,320 Hexose catabolic process 2.23 × 10−13 1.39 × 10−10
46,365 Monosaccharide catabolic process 3.82 × 10−13 1.60 × 10−10
46,164 Alcohol catabolic process 2.60 × 10−12 8.14 × 10−10
44,275 Cellular carbohydrate catabolic process 3.98 × 10−12 9.99 × 10−10
Protocol G
31,640 Killing of cells of another organism 2.23 × 10−6 8.75 × 10−4
7,398 Ectoderm development 6.97 × 10−6 1.37 × 10−3
1,906 Cell killing 1.13 × 10−5 1.48 × 10−3
9,888 Tissue development 4.50 × 10−5 4.41 × 10−3
8,544 Epidermis development 1.14 × 10−4 8.93 × 10−3

DyNet analysis of the hierarchical networks of GO cell component categories shows that the in-solution digestion covers a broader range of terms compared to the one-step in-solution digestion. While plasma lipoprotein particle, protein–lipid complex, high-density lipoprotein particle, fibrinogen complex, and spherical high-density lipoprotein particle cell component categories are specific to the one-step in-solution digestion, some entire branches of GO categories are specific to the second procedure. The saliva samples processed by the second procedure are enriched, e.g., by cell component GO terms involving membrane, extrinsic to membrane, plasma membrane phagocytic cup, membrane part, and plasma membrane part.

Significant differences in the networks were also found for GO categories of biological processes. There are few common categories of proteins identified in both samples. Most of them belong to biological processes specific to single networks, while, e.g., the entire branch of GO terms associated with metabolic processes is specific for the in-solution digestion (protocol B), the proteins generated by the procedure G manifest specificity for, e.g., response to a chemical stimulus.

3.2 Comparison of FASP protocols for Amicon and Microcon ultra-centrifugal unit

In pairwise mode supporting the presence or absence of node and edge attributes, the DyNet plug-in was applied to visualize differences in specific and shared categories corresponding to protein groups identified by FASP strategies. The network included 144 functional partners (nodes) and 1,272 edges. Nodes in green correspond to protocol C (Amicon 0.5 mL centrifugal filter units with MWCO of 30 kDa), nodes in red correspond to protocol D1 (Microcon 0.5 mL centrifugal filter units with MWCO of 30 kDa), and nodes in white color (shared with both) are shown in Figure 3.

Figure 3 
                  DyNet visualization of union gene/protein networks identified by FASP digestion strategies: protocol C only (Amicon 0.5 mL centrifugal filter units with MWCO of 30 kDa) ‒ green circles, only protocol D1 (Microcon 0.5 mL centrifugal filter units with MWCO of 30 kDa) ‒ red circles, both samples ‒ white circles.
Figure 3

DyNet visualization of union gene/protein networks identified by FASP digestion strategies: protocol C only (Amicon 0.5 mL centrifugal filter units with MWCO of 30 kDa) ‒ green circles, only protocol D1 (Microcon 0.5 mL centrifugal filter units with MWCO of 30 kDa) ‒ red circles, both samples ‒ white circles.

Enrichment maps were analyzed by looking for changes in samples prepared by FASP associated with cell component categories (not shown), molecular function (not shown), and biological process (not shown) assessed by GO. While 19 GO cell categories were found enriched for network A, 35 GO categories were found for network B. 50 and 58 GO molecular functions were found enriched for networks A and B, respectively. In the top 5 GO molecular function categories with the smallest p-values chosen as significant, the inhibitor/regulator activity of catalytically active enzymes predominates for both the networks.

The hierarchical networks revealed the diversity and interdependence of the biological processes related to GO. 151 and 237 GO biological process terms were found enriched according to protocols C and D1 used for FASP sample preparation, respectively. The top 5 GO categories of biological processes with the smallest p-values were selected as significant (Table 4).

Table 4

GO categories of biological processes with the smallest p-values for proteins identified in the saliva sample prepared by FASP using Amicon 0.5 mL centrifugal filter units with MWCO of 30 kDa ‒ protocol C and Microcon 0.5 mL centrifugal filter units with MWCO of 30 kDa ‒ protocol D1

GO-ID Description GO p-value Corr. p-value
Protocol C
6,007 Glucose catabolic process 1.55 × 10−14 1.85 × 10−11
19,320 Hexose catabolic process 1.19 × 10−13 7.11 × 10−11
46,365 Monosaccharide catabolic process 2.04 × 10−13 8.15 × 10−11
46,164 Alcohol catabolic process 1.39 × 10−12 4.17 × 10−10
44,275 Cellular carbohydrate catabolic process 2.14 × 10−12 5.12 × 10−10
Protocol D1
9,611 Response to wounding 1.05 × 10−15 1.32 × 10−12
6,950 Response to stress 1.56 × 10−14 9.83 × 10−12
6,952 Defense response 2.16 × 10−13 6.88 × 10−11
2,526 Acute inflammatory response 2.18 × 10−13 6.88 × 10−11
6,954 Inflammatory response 6.53 × 10−12 1.65 × 10−9

As shown in Table 4, proteins/genes identified according to protocol C were significantly associated with the catabolic processes. In the saliva samples processed using the D1 protocol, the scope of enriched biological processes was different compared to the previous one. Response to wounding, stress, and inflammatory response of proteins/genes identified by method D1 included in the list of biological process categories were found to be significant. A closer look at the inflammatory and acute inflammatory responses as a common implication in TMJ disorders revealed a significant difference in a number of identified peptides in compared sample preparation procedures. An identical FASP sample preparation protocol using the identical 30 kDa MWCO centrifugal filter but from a different manufacturer, showed surprisingly large differences (Figure 4).

Figure 4 
                  Bar graph depicting the arithmetic mean and standard deviation (error bars) of the number of identified proteins/peptides involved in GO biological process − inflammatory response. Each sample was measured in biological triplicate. An identical FASP sample preparation protocol was used for both manufacturers of MWCO 30 kDa filters (Amicon and Microcon).
Figure 4

Bar graph depicting the arithmetic mean and standard deviation (error bars) of the number of identified proteins/peptides involved in GO biological process − inflammatory response. Each sample was measured in biological triplicate. An identical FASP sample preparation protocol was used for both manufacturers of MWCO 30 kDa filters (Amicon and Microcon).

The hierarchical networks of GO categories were superimposed in one graph utilizing the DyNet plug-in to highlight specific and shared categories corresponding to protein groups identified by FASP protocols using an Amicon and/or Microcon ultra-centrifugal unit (Figure 5).

Figure 5 
                  The hierarchical DyNet networks of GO categories: (a) GO cell component, (b) GO molecular function, and (c) GO biological process. Green circles – GO categories identified only by FASP protocol D1 (Microcon), red circles – GO categories identified only by FASP protocol C (Amicon), and white circles – GO categories identified in both samples.
Figure 5

The hierarchical DyNet networks of GO categories: (a) GO cell component, (b) GO molecular function, and (c) GO biological process. Green circles – GO categories identified only by FASP protocol D1 (Microcon), red circles – GO categories identified only by FASP protocol C (Amicon), and white circles – GO categories identified in both samples.

The Amicon ultra-centrifugal unit preferentially extracted from saliva those proteins associated with the plasma membrane (Figure 5a – group I). The network branch (Figure 5a – group II) (chylomicron, mature chylomicron, very-low-density lipoprotein particle, triglyceride-rich lipoprotein particle, spherical high-density lipoprotein particle, and plasma lipoprotein particle) was specific to Microcon ultra-centrifugal unit.

Regarding the differences in the molecular function categories, the Microcon-assisted preparation of the proteomic sample showed pronounced specific terms responsible for binding, while the Amicon-assisted procedure resulted in a prevalence of proteins that are responsible for, e.g., enzyme regulator activity (Figure 5b – group III).

Significant differences in the networks were also found for GO categories of biological processes (Figure 5c). While about 50% of the biological process terms were common to both filter-assisted sample preparation, the rest represented the specific processes with dominance of categories generated from the Microcon-assisted sample preparation, e.g., group of lipid transport processes (Figure 5c – group IV). Specific GO categories of biological processes for the Amicon-assisted sample preparation included, e.g., nucleoside metabolic processes (Figure 5c – group V).

3.3 Comparison of in-solution and FASP strategies of digestion of amylase-depleted saliva

The hierarchical networks of GO categories were superimposed in a graph generated in the DyNet application to highlight specific and common categories corresponding to protein groups identified in amylase-depleted saliva digested with FASP or in-solution digestion protocols (Figure 6).

Figure 6 
                  The hierarchical DyNet networks of GO biological process. Green circles – GO categories identified only in amylase-depleted saliva samples prepared by in-solution protocol B, red circles – GO categories identified only in amylase-depleted saliva samples prepared according to FASP protocol D2, and white circles – GO categories identified in both samples.
Figure 6

The hierarchical DyNet networks of GO biological process. Green circles – GO categories identified only in amylase-depleted saliva samples prepared by in-solution protocol B, red circles – GO categories identified only in amylase-depleted saliva samples prepared according to FASP protocol D2, and white circles – GO categories identified in both samples.

DyNet analysis of the hierarchical networks of GO cell component categories showed that the FASP strategy for amylase-depleted saliva covered a broader range of categories compared to the in-solution digestion procedure. However, the in-solution digestion procedure of amylase-depleted saliva resulted in a significantly increased number of identified proteins (9 vs 14 identified proteins and 34 vs 55 identified peptides, respectively) in the immune response and cytokine signaling pathway (Figure 1). While in-solution digestion did not provide any additional specific GO cell component categories, the FASP method helped identify proteins from intracellular non-membrane-bounded organelle, cortical cytoskeleton, cell cortex, cell cortex part, non-membrane-bounded organelle, and cytoskeleton.

A similar picture emerged for GO molecular function categories. The specific categories identified with the aid of FASP-assisted preparation of an amylase-depleted saliva sample predominated.

Differences in the networks were also found for GO categories of biological processes (Figure 6). The DyNet network was enriched, e.g., by the terms of developmental process (Figure 6 – group I) specific for amylase-depleted saliva prepared by the FASP protocol and the group of ion transport (Figure 6 – group II) specific for amylase-depleted saliva prepared by the in-solution method.

3.4 Comparison of samples prepared by FASP/Microcon digestion at 37 and 24°C

To investigate the potential impact of a digestion temperature lower than the typical 37°C on the samples prepared according to the FASP/Microcon digestion protocol, the hierarchical networks were constructed using the DyNet application in the Cytoscape environment.

GO categories were overlaid on a graph to highlight specific and shared categories corresponding to protein groups identified in saliva digested using FASP protocol E in the presence of 80% ACN.

According to the DyNet networks of GO cell component categories, digestion of salivary proteins at 24°C encompassed a narrower range of categories compared to 37°C. Digestion at 24°C yielded some additional categories such as cortical cytoskeleton, cell cortex part, endoplasmic reticulum lumen, cell cortex, endoplasmic reticulum part, subsynaptic reticulum, endoplasmic reticulum, and intracellular organelle lumen. Digestion at 37°C resulted in the prevalence of proteins characterized by categories of GO cell component, e.g., membrane, membrane part, plasma membrane, protein complex, plasma membrane part, MHC protein complex, MHC class I protein complex, catalytic activity, cell junction, anchoring junction, and cell-cell junction or desmosome.

Regarding the differences in GO molecular function categories, the digestion of a proteomic sample at 24°C revealed different specific terms, which correspond to, e.g., lyase activity or output of multiple GO terms of receptor activity.

Significant differences in the networks were also found for GO biological process terms. While, e.g., the entire branch of the development process is specific to the sample preparation at 24°C, the other branch was exclusively observed for a digestion temperature of 37°C with multiple terms of regulation of metabolic process.

3.5 Comparison of samples prepared by FASP/Microcon one-step digestion at 37 and 24°C

The temperature lower than the typical digestion temperature of 37°C was also studied on the samples prepared by the FASP/Microcon one-step digestion performed in an organic-aqueous environment (80% ACN), where the steps of protein reduction and alkylation were omitted. The one-step digestion protocol reduced the number of proteins identified by up to 50%, and the number of proteins identified in the signaling pathways monitored was similarly reduced.

The hierarchical networks were used to overlay GO categories in a graph to draw attention to specific and common categories corresponding to protein groups found in saliva digested with FASP digestion protocol F.

The DyNet networks of GO cell component categories were mainly enriched for the procedure performed at 24°C compared to 37°C, which did not provide additional specific categories. Digestion at 24°C revealed the prevalence of proteins characterized by GO cell component categories, e.g., spherical high-density lipoprotein particle, high-density lipoprotein particle, plasma lipoprotein particle, protein–lipid complex or contractile fiber part, myofibril, and contractile fiber.

Concerning the dissimilarities in GO molecular function categories, digestion of a proteomic sample at 24°C yielded different specific terms responsible for, e.g., lyase activity, or output of multiple GO terms of protein binding. The network of the sample digested at 37°C showed some complete branches of GO molecular function categories that were not detected in the sample processed at 24°C, including nuclease activity terms.

The unified network for GO biological process categories showed the dominance of terms generated from the one-step FASP digestion at 24°C, e.g., branch of regulation of homeostasis processes.

4 Discussion

In terms of protein identification (Table 2), the results showed that in-solution digestion of unfractionated as well as amylase-depleted saliva prepared according to protocol B and FASP sample preparation method D2 yielded the highest number of comparable proteins. In general, more consistent results in terms of inter-sample reproducibility of protein identification were obtained with in-solution protocol B (about 80%) than with FASP protocol C and D (between 70 – 75%).

Due to the broad dynamic range of salivary proteins, a starch-depleted strategy to enrich the proteomic sample for less abundant proteins has proven to be an effective and simple method [47]. In our experiments, the affinity-based amylase depletion of saliva also resulted in an increase in the number of identified proteins, as reported by authors of the paper [47]. However, it should be noted that the two methods of preparing proteomics samples yielded qualitatively different results, as shown by network analysis. In the future, complementary methods could be used to enrich saliva samples with low-expression proteins [48].

In all FASP protocols, we have used AB or the combination of AB with ACN because not only is the product of trypsinolysis not contaminated by urea or other non-volatile salts, but it can also be dried in a vacuum concentrator to yield a mixture of peptides without volatile compounds [49].

The number of proteins generated by FASP methods has been shown to depend on the protein loading of the spin filter. For both filter types used (Amicon and Microcon), the 50-µg load has proven to be the most effective for protein digestion. While processing small amounts of protein can lead to sample loss through non-specific binding of proteins to the surfaces of the spin filter unit, additional sample loss can lead to irreversible protein aggregations arising from increased protein concentrations [50,51]. It is possible that the unsatisfactory yield of saliva prefractionated on the Amicon Ultra-4 centrifugal filter unit with a cut-off of 50 kDa was due to the use of large volumes of saliva (5 mL), resulting in membrane overload and loss due to the protein aggregation at their high concentrations, which increase during ultra-centrifugation (Protocol A − not shown in Figure 1).

No significant differences were observed for the number of proteins identified for FASP using the Microcon flat-bottom filter and the Amicon filter under the same protein loadings and identical enzymatic trypsin digestion conditions, despite findings that the Microcon ultra-centrifugal unit may lead to 10.5 and 9.5% loss in proteome-level peptide and protein identification, respectively [52]. Nevertheless, there are large differences in the quality of the outcomes and the proteome coverage when comparing the FASP for both membrane filters, as indicated by the network analysis.

In the present work, we tested two different proteolysis temperatures in addition to the usual digestion at 37°C; we also performed the enzymatic digestion at 24°C [53]. Proteolysis performed near room temperature improved the number of identified proteins only for the FASP protocol with ACN-assisted trypsin digestion, when 80% ACN was present in the reaction mixtures. However, the range of locations relative to cellular structures was larger for 37°C.

In comparison to 24°C digestion temperature, GO cell component analysis showed that digestion at 37°C resulted in the abundance of proteins classified by categories of GO cell component, such as membrane, membrane part, plasma membrane, and plasma membrane part.

It is without dispute that membrane proteins are essential for biological processes. They are significant for the transport of chemical compounds, environmental sensing as receptors, signal transduction, and cell-to-cell communication [54].

However, for proteomics analysis, they are difficult to work with and digest. Membrane proteins’ hydrophobicity, poor solubility, and low abundance limit proteomic techniques from detecting them. The reason is that for instance, integral membrane proteins are underrepresented in conventional proteomic investigations in the absence of trypsin cleavage sites in their transmembrane regions [55].

Numerous alternative strategies have been developed to promote membrane protein digestion, including the use of high temperatures, microwave radiation, or ultrasound [56,57,58].

For the release of peptides arising from a cleavage in extramembrane loops of membrane proteins with numerous transmembrane regions, the solubilization phase is crucial [55]. Higher temperature during the preparation of proteomic samples proved to be an effective factor that increases the solubilization of membrane proteins and enables their effective identification during proteomic analysis. Based on previous reports, implementation of elevated digestion temperature should be kept in mind for enhancing the identification of hydrophobic peptides. Recently, the experiments with the use of trypsin stabilized by Ca2+ ions in the presence of sodium dodecyl sulfate led to enhanced membrane protein identification and increased digesting efficiency at elevated temperature of 37°C [59].

We also examined alternative methods that can be used to process samples quickly and effectively. FASP protocols with digestion in the presence of 80% ACN and one-step conversion of saliva to tryptic digest in a single tube were used. The one-step digestion was performed in the presence of 80% ACN, omitting the protein reduction and alkylation steps for both in-solution and FASP digestion strategies. Attempting to perform proteolysis in an organic-aqueous solvent system using the simplest procedure has proven inefficient compared to other strategies, despite the experience of some authors [22,60,61]. Nevertheless, in our experiments, the 80% content of the organic solvent could reduce the trypsin activity and decrease the content of some peptides.

Problems with the detection and identification of proteins in saliva could be alternatively overcome by innovative methods based on solid phase microextraction that reduces potential errors and prevents the consequences of analyte instability [33,35,36,62,63].

The analysis of the networks according to the individual sample preparation methods for the bottom-up investigation using LC-EIS-MS demonstrated the great variability in the protein properties. Because the hierarchical networks of GO categories are highly dependent on the method used, the procedure can be easily tailored to the specific samples and protein groups under investigation.


tel: +421-55-642-9055, fax: +421-55-234-3250

  1. Funding information: This research was conducted with support from the Scientific Grant Agency of the Slovak Republic under grant number 1/0196/20.

  2. Author contributions: V.S. – conceptualization and resources. I.T. – conceptualization, funding acquisition, investigation, and writing – review and editing. G.L. – conceptualization, formal analysis, investigation, writing – original draft, and writing – review and editing.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Ethical approval: The study protocol was approved by the Ethics Committee of the Louis Pasteur University Hospital in Košice (Approval 2020/EK/06046).

  5. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

[1] Proctor GB. The physiology of salivary secretion. Periodontol 2000. 2016;70(1):11–25. 10.1111/prd.12116.Search in Google Scholar PubMed

[2] Loo JA, Yan W, Ramachandran P, Wong DT. Comparative human salivary and plasma proteomes. J Dent Res. 2010;89(10):1016–23. 10.1177/0022034510380414.Search in Google Scholar PubMed PubMed Central

[3] Pappa E, Vougas K, Zoidakis J, Vastardis H. Proteomic advances in salivary diagnostics. BBA-Proteins Proteom. 2020;1868(11):140494. 10.1016/j.bbapap.2020.140494.Search in Google Scholar PubMed

[4] Sembler-Møller ML, Belstrøm D, Locht H, Pedersen AML. Proteomics of saliva, plasma, and salivary gland tissue in Sjögren’s syndrome and non-Sjögren patients identify novel biomarker candidates. J Proteom. 2020;225:103877. 10.1016/j.jprot.2020.103877.Search in Google Scholar PubMed

[5] Viswanath B, Choi CS, Lee K, Kim S. Recent trends in the development of diagnostic tools for diabetes mellitus using patient saliva. Trac-Trend Anal Chem. 2017;89:60–7. 10.1016/j.trac.2017.01.011.Search in Google Scholar

[6] Wang X, Kaczor-Urbanowicz KE, Wong DT. Salivary biomarkers in cancer detection. Med Oncol. 2017;34(1):7. 10.1007/s12032-016-0863-4.Search in Google Scholar PubMed PubMed Central

[7] Bellagambi FG, Lomonaco T, Salvo P, Vivaldi F, Hangouët M, Ghimenti S, et al. Saliva sampling: Methods and devices. An overview. Trends Anal Chem. 2020;124:115781. 10.1016/j.trac.2019.115781.Search in Google Scholar

[8] Yaman D, Alpaslan C, Akca G, Avcı E. Correlation of molecular biomarker concentrations between synovial fluid and saliva of the patients with temporomandibular disorders. Clin Oral Invest. 2020;24(12):4455–61. 10.1007/s00784-020-03310-8.Search in Google Scholar PubMed

[9] Jasim H, Ernberg M, Carlsson A, Gerdle B, Ghafouri B. Protein signature in saliva of temporomandibular disorders myalgia. Int J Mol Sci. 2020;21(7):2569. 10.3390/ijms21072569.Search in Google Scholar PubMed PubMed Central

[10] El Moussaoui S, Mallandrich M, Garrós N, Calpena AC, Rodríguez Lagunas MJ, Fernández-Campos F. HPV lesions and other issues in the oral cavity treatment and removal without pain. Int J Mol Sci. 2021;22(20):11158. 10.3390/ijms222011158.Search in Google Scholar PubMed PubMed Central

[11] Ibi M. Inflammation and temporomandibular joint derangement. Biol Pharm Bull. 2019;42(4):538–42. 10.1248/bpb.b18-00442.Search in Google Scholar

[12] Ulmner M, Sugars R, Naimi-Akbar A, Alstergren P, Lund B. Cytokines in temporomandibular joint synovial fluid and tissue in relation to inflammation. J Oral Rehabil. 2022;49(6):599–607. 10.1111/joor.13321.Search in Google Scholar

[13] Hollander JM, Zeng L. The emerging role of glucose metabolism in cartilage development. Curr Osteoporos Rep. 2019;17(2):59–69. 10.1007/s11914-019-00506-0.Search in Google Scholar

[14] Dupree EJ, Jayathirtha M, Yorkey H, Mihasan M, Petre BA, Darie CC. A critical review of bottom-up proteomics: The good, the bad, and the future of this field. Proteomes. 2020;8(3):14. 10.3390/proteomes8030014.Search in Google Scholar

[15] Ye X, Tang J, Mao Y, Lu X, Yang Y, Chen W, et al. Integrated proteomics sample preparation and fractionation: Method development and applications. Trac-Trend Anal Chem. 2019;120:115667. 10.1016/j.trac.2019.115667.Search in Google Scholar

[16] Kim YI, Cho JY. Gel-based proteomics in disease research: Is it still valuable? BBA-Proteins Proteom. 2019;1867(1):9–16. 10.1016/j.bbapap.2018.08.001.Search in Google Scholar

[17] Marcus K, Lelong C, Rabilloud T. What room for two-dimensional gel-based proteomics in a shotgun proteomics world? Proteomes. 2020;8(3):17. 10.3390/proteomes8030017.Search in Google Scholar

[18] Medzihradszky KF. In-solution digestion of proteins for mass spectrometry. Methods Enzymol. 2005;405:50–65. 10.1016/S0076-6879(05)05003-2.Search in Google Scholar

[19] Zhang X, Sadowski P, Punyadeera C. Evaluation of sample preparation methods for label-free quantitative profiling of salivary proteome. J Proteom. 2020;210:103532. 10.1016/j.jprot.2019.103532.Search in Google Scholar PubMed

[20] Liu F, Ye M, Pan Y, Zhang Y, Bian Y, Sun Z, et al. Integration of cell lysis, protein extraction, and digestion into one step for ultrafast sample preparation for phosphoproteome analysis. Anal Chem. 2014;86(14):6786–91. 10.1021/ac5002146.Search in Google Scholar PubMed

[21] Chen Q, Yan G, Gao M, Zhang X. Direct digestion of proteins in living cells into peptides for proteomic analysis. Anal Bioanal Chem. 2015;407(3):1027–32. 10.1007/s00216-014-8173-1.Search in Google Scholar PubMed

[22] Laštovičková M, Bobál P, Strouhalová D, Bobálová J. Acetonitrile-assisted enzymatic digestion can facilitate the bottom-up identification of proteins of cancer origin. Anal Biochem. 2019;570:1–4. 10.1016/j.ab.2019.01.004.Search in Google Scholar PubMed

[23] Crowell AM, Stewart EJ, Take ZS, Doucette AA. Critical assessment of the spectroscopic activity assay for monitoring trypsin activity in organic-aqueous solvent. Anal Biochem. 2013;435(2):131–6. 10.1016/j.ab.2012.12.019.Search in Google Scholar PubMed

[24] Wall MJ, Crowell AM, Simms GA, Carey GH, Liu F, Doucette AA. Implications of partial tryptic digestion in organic-aqueous solvent systems for bottom-up proteome analysis. Anal Chim Acta. 2011;703(2):194–203. 10.1016/j.aca.2011.07.025.Search in Google Scholar PubMed

[25] Manza LL, Stamer SL, Ham AJ, Codreanu SG, Liebler DC. Sample preparation and digestion for proteomic analyses using spin filters. Proteomics. 2005;5(7):1742–5. 1010.1002/pmic.200401063.Search in Google Scholar

[26] Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6(5):359–62. 1010.1038/nmeth.1322.Search in Google Scholar

[27] Doellinger J, Schneider A, Hoeller M, Lasch P. Sample preparation by easy extraction and digestion (SPEED) – A universal, rapid, and detergent-free protocol for proteomics based on acid extraction. Mol Cell Proteom. 2020;19(1):209–22. 10.1074/mcp.TIR119.001616.Search in Google Scholar PubMed PubMed Central

[28] Tremblay TL, Hill JJ. Adding polyvinylpyrrolidone to low level protein samples significantly improves peptide recovery in FASP digests: An inexpensive and simple modification to the FASP protocol. J Proteom. 2021;230:104000. 10.1016/j.jprot.2020.104000.Search in Google Scholar PubMed

[29] Erde J, Loo RR, Loo JA. Enhanced FASP (eFASP) to increase proteome coverage and sample recovery for quantitative proteomic experiments. J Proteome Res. 2014;13(4):1885–95. 10.1021/pr4010019.Search in Google Scholar PubMed PubMed Central

[30] Goryński K, Goryńska P, Górska A, Harężlak T, Jaroch A, Jaroch K, et al. SPME as a promising tool in translational medicine and drug discovery: From bench to bedside. J Pharm Biomed Anal. 2016;130:55–67. 10.1016/j.jpba.2016.05.012.Search in Google Scholar PubMed

[31] Kataoka H. In-tube solid-phase microextraction: Current trends and future perspectives. J Chromatogr A. 2021;1636:461787. 10.1016/j.chroma.2020.461787.Search in Google Scholar PubMed

[32] Xu L, Hu ZS, Duan R, Wang X, Yang YS, Dong LY, et al. Advances and applications of in-tube solid-phase microextraction for analysis of proteins. J Chromatogr A. 2021;1640:461962. 10.1016/j.chroma.2021.461962.Search in Google Scholar PubMed

[33] Bessonneau V, Boyaci E, Maciazek-Jurczyk M, Pawliszyn J. In vivo solid phase microextraction sampling of human saliva for non-invasive and on-site monitoring. Anal Chim Acta. 2015;856:35–45. 10.1016/j.aca.2014.11.029.Search in Google Scholar PubMed

[34] Wu L, Yuan ZC, Li ZM, Huang Z, Hu B. In vivo solid-phase microextraction swab sampling of environmental pollutants and drugs in human body for nano-electrospray ionization mass spectrometry analysis. Anal Chim Acta. 2020;1124:71–7. 10.1016/j.aca.2020.05.022.Search in Google Scholar PubMed

[35] Wu L, Yuan ZC, Yang BC, Huang Z, Hu B. In vivo solid-phase microextraction swab-mass spectrometry for multidimensional analysis of human saliva. Anal Chim Acta. 2021;1164:338510. 10.1016/j.aca.2021.338510.Search in Google Scholar PubMed

[36] Hughes CS, Moggridge S, Müller T, Sorensen PH, Morin GB, Krijgsveld J. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat Protoc. 2019;14(1):68–85. 10.1038/s41596-018-0082-x.Search in Google Scholar PubMed

[37] Sivadasan P, Gupta MK, Sathe GJ, Balakrishnan L, Palit P, Gowda H, et al. Human salivary proteome - a resource of potential biomarkers for oral cancer. J Proteom. 2015;127(Pt A):89–95. 10.1016/j.jprot.2015.05.039.Search in Google Scholar PubMed

[38] Thomadaki K, Helmerhorst EJ, Tian N, Sun X, Siqueira WL, Walt DR, et al. Whole-saliva proteolysis and its impact on salivary diagnostics. J Dent Res. 2011;90(11):1325–30. 10.1177/0022034511420721.Search in Google Scholar PubMed PubMed Central

[39] Laputková G, Talian I, Schwartzová V, Schwartzová Z. MALDI-TOF MS profiling in the discovery and identification of salivary proteomic patterns of temporomandibular joint disorders. Open Chem. 2020;18(1):1173–80. 10.1515/chem-2020-0174.Search in Google Scholar

[40] 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. 10.1002/elps.200800207.Search in Google Scholar PubMed

[41] Sun Y, Xia Z, Shang Z, Sun K, Niu X, Qian L, et al. Facile preparation of salivary extracellular vesicles for cancer proteomics. Sci Rep. 2016;6:24669. 10.1038/srep24669.Search in Google Scholar PubMed PubMed Central

[42] Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. 10.1038/nprot.2008.211.Search in Google Scholar PubMed

[43] Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. 10.1101/gr.1239303.Search in Google Scholar PubMed PubMed Central

[44] Goenawan IH, Bryan K, Lynn DJ. DyNet: visualization and analysis of dynamic molecular interaction networks. Bioinformatics. 2016;32(17):2713–5. 10.1093/bioinformatics/btw187.Search in Google Scholar PubMed PubMed Central

[45] Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21(16):3448–9. 10.1093/bioinformatics/bti551.Search in Google Scholar PubMed

[46] Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape StringApp: Network analysis and visualization of proteomics data. J Proteome Res. 2019;18(2):623–32. 10.1021/acs.jproteome.8b00702.Search in Google Scholar PubMed PubMed Central

[47] Krief G, Deutsch O, Zaks B, Wong DT, Aframian DJ, Palmon A. Comparison of diverse affinity based high-abundance protein depletion strategies for improved bio-marker discovery in oral fluids. J Proteom. 2012;75(13):4165–75. 10.1016/j.jprot.2012.05.012.Search in Google Scholar PubMed

[48] Huang H, Mackeen MM, Cook M, Oriero E, Locke E, Thézénas ML, et al. Proteomic identification of host and parasite biomarkers in saliva from patients with uncomplicated Plasmodium falciparum malaria. Malar J. 2012;11:178. 10.1186/1475-2875-11-178.Search in Google Scholar PubMed PubMed Central

[49] Kim SC, Chen Y, Mirza S, Xu Y, Lee J, Liu P, et al. A clean, more efficient method for in-solution digestion of protein mixtures without detergent or urea. J Proteome Res. 2006;5(12):3446–52. 10.1021/pr0603396.Search in Google Scholar PubMed

[50] Wiśniewski JR. Filter-aided sample preparation: The versatile and efficient method for proteomic analysis. Methods Enzymol. 2017;585:15–27. 10.1016/bs.mie.2016.09.013.Search in Google Scholar PubMed

[51] Wiśniewski JR. Quantitative evaluation of filter aided sample preparation (FASP) and multienzyme digestion FASP protocols. Anal Chem. 2016;88(10):5438–43. 10.1021/acs.analchem.6b00859.Search in Google Scholar PubMed

[52] Tang L, Wu Z, Wang J, Zhang X. Formaldehyde derivatization: An unexpected side reaction during filter-aided sample preparation. Anal Chem. 2020;92(18):12120–5. 10.1021/acs.analchem.0c01981.Search in Google Scholar PubMed

[53] Betancourt LH, Sanchez A, Pla I, Kuras M, Zhou Q, Andersson R, et al. Quantitative assessment of urea in-solution lys-C/trypsin digestions reveals superior performance at room temperature over traditional proteolysis at 37°C. J Proteome Res. 2018;17(7):2556–61. 10.1021/acs.jproteome.8b00228.Search in Google Scholar PubMed

[54] Tan S, Tan HT, Chung MC. Membrane proteins and membrane proteomics. Proteomics. 2008;8(19):3924–32. 10.1002/pmic.200800597.Search in Google Scholar PubMed

[55] Vit O, Petrak J. Integral membrane proteins in proteomics. How to break open the black box? J Proteom. 2017;153(5):8–20. 10.1016/j.jprot.2016.08.006.Search in Google Scholar PubMed

[56] Ye X, Li L. Microwave-assisted protein solubilization for mass spectrometry-based shotgun proteome analysis. Anal Chem. 2012;84(14):6181–91. 10.1021/ac301169q.Search in Google Scholar PubMed

[57] Lopez-Ferrer D, Hixson KK, Belov ME, Smith RD. Ultra-fast sample preparation for high-throughput proteomics. In: Ivanov A, Lazarev A, editors. Sample preparation in biological mass spectrometry. Dordrecht: Springer; 2011. p. 129–35. 10.1007/978-94-007-0828-0_8.Search in Google Scholar

[58] Moore SM, Hess SM, Jorgenson JW. Extraction, enrichment, solubilization, and digestion techniques for membrane proteomics. J Proteome Res. 2016;15(4):1243–52. 10.1021/acs.jproteome.5b01122.Search in Google Scholar PubMed PubMed Central

[59] Alhumaidan O, Bottrill AR, Mistry SC, Andrew P, Mukamolova GV, Turapov O. Efficient protein digestion at elevated temperature in the presence of sodium dodecyl sulfate and calcium ions for membrane proteomics. Anal Chem. 2019;91(15):9516–21. 10.1021/acs.analchem.9b00484.Search in Google Scholar PubMed

[60] Strader MB, Tabb DL, Hervey WJ, Pan C, Hurst GB. Efficient and specific trypsin digestion of microgram to nanogram quantities of proteins in organic-aqueous solvent systems. Anal Chem. 2006;78(1):125–34. 10.1021/ac051348l.Search in Google Scholar PubMed

[61] Liu CC, Liang LH, Yang Y, Yu HL, Yan L, Li XS, et al. Direct acetonitrile-assisted trypsin digestion method combined with LC-MS/MS-targeted peptide analysis for unambiguous identification of intact ricin. J Proteome Res. 2021;20(1):369–80. 10.1021/acs.jproteome.0c00458.Search in Google Scholar PubMed

[62] Kahremanoglu K, Temel ER, Korkut TE, Nalbant AA, Azer BB, Durucan C, et al. Development of a solid-phase microextraction LC-MS/MS method for determination of oxidative stress biomarkers in biofluids. J Sep Sci. 2020;43(9–10):1925–33. 10.1002/jssc.202000211.Search in Google Scholar PubMed

[63] Ölander C, Edvardsson Rasmussen J, Eriksson PO, Laurell G, Rask-Andersen H, Bergquist J. The proteome of the human endolymphatic sac endolymph. Sci Rep. 2021;11(1):11850. 10.1038/s41598-021-89597-3.Search in Google Scholar PubMed PubMed Central

Received: 2022-07-21
Revised: 2022-09-14
Accepted: 2022-09-15
Published Online: 2022-10-06

© 2022 Vladimíra Schwartzová et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Downloaded on 2.2.2023 from https://www.degruyter.com/document/doi/10.1515/chem-2022-0216/html
Scroll Up Arrow