ABSTRACT
Proteins and peptides indicate physiological or pathological states and are investigated to identify markers to scrutinize health and disease surveillance. Saliva contains many proteins and peptides that could serve as biomarkers, offering a potential noninvasive approach for disease detection. To enable the assessment of the saliva proteome and peptidome as sources of biomarkers, protocols for sampling, sample preparation, and measurements have to be developed. We present the results of peptidome analysis from saliva samples collected at different time points before and after breakfast from 14 healthy adults (50% male, mean age 42.7 ± 10.3 years). While similar methods have been previously applied to urine, our aim was to adapt and demonstrate the effectiveness of these protocols for saliva. Specifically, we aimed to establish a salivary peptide dataspace, including peptide amino acid sequences, and to evaluate the impact of food intake and time of sampling. Capillary electrophoresis‐mass spectrometry (CE‐MS) and CE‐MS/MS were used for peptidome analysis. Per sample, 3147 ± 559 peptides were detectable, without significant differences in the number of detected peptides between sample collection times. However, some peptides differed significantly in their abundance between samples collected before breakfast and 1, 2 and 4 h after breakfast. Samples collected after breakfast were more consistent in their peptide content. Sequencing identified 630 peptides, fragments of 82 proteins, with the majority derived from proline‐rich proteins. The data indicate that saliva for peptidomics is best collected 1 to 4 h after breakfast.
Keywords: biomarkers, mass spectrometry, peptides, proteomics, saliva
Abbreviations
- CE‐MS
capillary electrophoresis‐mass spectrometry
- COL3A1
collagen alpha‐1(III)
- FDR
false discovery rate
- PRP
basic salivary proline‐rich protein
- PRPC
salivary acidic proline‐rich phosphoprotein 1/2
- PSM
peptide‐spectrum match
- WMS
whole mouth saliva
1. Introduction
Peptidome analysis of urine using capillary electrophoresis‐mass spectrometry (CE‐MS) is an established method that has been applied in multiple studies for diagnosis and prognosis of diseases in both adults and children [1, 2, 3, 4, 5, 6, 7, 8]. The application of this technology platform to other body fluids, such as plasma and cerebrospinal fluid, has also been demonstrated [9, 10], indicating that the approach can be generalized for the analysis of various body fluids. Saliva has been proposed as a valuable source of disease‐specific biomarkers for several human conditions. The diagnostic potential of saliva‐based biosensors was recently highlighted by Min et al. [11] and previously by Castagnola et al. [12]. Proteomics and peptidomics studies focusing on saliva have identified potential biomarkers for early disease detection, disease progression, monitoring, and response to treatment in both oral and systemic diseases [13−19].
Whole mouth saliva (WMS) is a complex fluid composed of secretions from major and minor salivary glands, gingival crevicular fluid, the oral microbiome, and dietary components. The method of saliva collection, whether whole or glandular, unstimulated or stimulated, can influence the salivary proteome, leading to variations in the relative abundance of specific proteins, as reported by Jasim et al. [20], Walz et al. [21], and Siqueira et al. [22]. Qunitana et al. demonstrated substantial variability in the saliva proteome depending on the time of day at which samples were collected [23]. These findings highlight the importance of standardized and consistent saliva collection methods to ensure comparability in proteomic analyses across different research groups.
Based on previous experience with saliva cortisol analysis in children and adults by Törnhage CJ [24−27] we aimed to initiate a peptidomics study in saliva. While peptidomics protocols have been established for urine, adapting these methods for saliva presents unique challenges. The impact of factors such as food intake and the timing of sample collection on the saliva proteome and peptidome remains largely unexplored. Therefore, we conducted a pilot study in healthy adult volunteers to develop a standardized protocol for saliva sampling (including optimal collection timepoint), sample preparation, and peptidome (CE‐MS) analysis. Additionally, this study aimed to define the saliva peptidome dataspace for subsequent investigations.
2. Materials and Methods
2.1. Sampling
Fourteen healthy (not taking any medication) adults (50% male, mean age 42.7 ± 10.3 years) were invited to participate in the study. All volunteers provided their approval to participate. The study was approved by the Mosaiques ad hoc Ethics Committee (Hannover, Germany) under reference number 01/2024. To implement a reproducible procedure, and based on preliminary results, samples were collected using the Salivette (Sarstedt, Germany) according to the instructions for use. Briefly, the Salivette collection device was kept in the mouth for 3 min, immediately centrifuged (2 min, 1000 × g) after removal, and the sample was stored at −80°C until further use. WMS was collected in the morning at four time points:
before breakfast,
1 h after breakfast,
2 h after breakfast, and
4 h after breakfast.
2.2. Sample Preparation
The protocol followed the previously established procedure for urine samples [28, 29]. Briefly, saliva samples were thawed immediately before use and treated with 1:1000 Thermo Scientific PMSF (phenylmethylsulfonyl fluoride) as a protease inhibitor. A 0.7 mL aliquot of saliva was diluted with 0.7 mL of 2 M urea, and 10 mM NH4OH containing 0.02% SDS. To remove higher molecular mass proteins, the sample was ultrafiltered using Centrisart ultracentrifugation filter devices (20 kDa MWCO) (Satorius, Göttingen, Germany) at 3000 rpm until 1.1 mL of filtrate was obtained. This filtrate was then applied onto a PD‐10 desalting column (GE Healthcare Bio Sciences, Uppsala, Sweden) equilibrated in 0.01% NH4OH in HPLC‐grade H2O to decrease matrix effects and enrich polypeptides. Finally, all samples were lyophilized, stored at 4°C, and suspended in 20 µL HPLC‐grade H2O shortly before CE‐MS analysis.
2.3. CE‐MS Analysis
CE‐MS analyses were performed essentially as described [28] using a P/ACE MDQ capillary electrophoresis system (Beckman Coulter, Fullerton, USA) on‐line coupled to a microTOF II MS (Bruker Daltonics, Bremen, Germany). Before running each sample, the uncoated fused silica capillary was conditioned using a multi‐step washing procedure. First, a 1 M NaOH solution (prepared in demineralized water) was flushed through the capillary at a pressure of 50 psi for 10 min. This was followed by a final wash with a 1.88% NH₄OH solution at 50 psi for 10 min. The capillary was then rinsed with the running buffer (0.94% formic acid (Sigma‐Aldrich), and 20% acetonitrile (Sigma‐Aldrich, Taufkirchen, Germany) in HPLC‐grade water) at 50 psi for 20 min. Following this conditioning process, the capillary (90 cm length, 50 µm inner diameter) was connected to the MS. The Beckman CE system was operated in reverse polarity mode, ensuring compatibility between the CE separation and the MS ionization voltage. Before each sample injection, the capillary was flushed with the running buffer for 2 min at 50 psi. Samples were injected into the CE‐MS system at a pressure of 2 psi for 99 s, corresponding to an injection volume of approximately 290 nL. Separation of analytes was performed by applying a voltage of +25 kV for 30 min at a capillary temperature of 35°C. Additionally, a stepwise pressure gradient was applied during separation: 0.1 psi for 1 min 0.2 psi for 1 min, 0.3 psi for 1 min, 0.4 psi for 1 min, and 0.5 psi for the remaining 30 min. Sheath liquid, consisting of 30% 2‐propanol and 0.4% formic acid in HPLC‐grade water, was applied coaxially at a flow rate of 0.02 mL/h (without nebulizer gas). The CE‐MS analysis was performed using an electrospray ionization (ESI) interface (Agilent Technologies, Palo Alto, CA, USA), with an applied potential ranging from −4.0 to −5.0 kV. The ESI sprayer was grounded to achieve an electric potential of zero. Mass spectra were recorded over an m/z range of 400–3000, with data accumulation every 3 s for approximately 60 min.
2.4. CE‐MS Data Processing
Mass spectral peaks representing identical molecules at different charge states were deconvoluted into single masses using MosaiquesVisu software [30]. Only signals with z > 1 observed in a minimum of 3 consecutive spectra, with a signal‐to‐noise ratio ≥ 4, were considered. Reference signals of 922 peptides were used for CE‐time and mass calibration. All detected peptides were deposited, matched, and annotated in a Microsoft SQL database, allowing further statistical analysis. For clustering, peptides in different samples were considered identical if mass deviation was below ±25 ppm. CE migration time was controlled to be below 5% after calibration. To enable semi‐quantitative comparison across samples, all peptide intensities were normalized using ppm normalization for each individual run, supporting comparability of detected peptide abundances.
2.5. Peptide Amino Acid Sequence Assignment
The amino acid sequences were obtained by performing MS/MS analysis on a P/ACE CE system coupled to a Q Exactive Plus Hybrid Quadrupole‐Orbitrap MS instrument (ThermoFisher Scientific, Waltham, Massachusetts, USA). The CE‐MS/MS analysis was performed using essentially the same interface as described above for CE‐MS [31], shown in Figure S1. The mass spectrometer was operated in MS/MS mode, as follows: (1) full MS: resolution of 70000, automatic gain control (AGC) target at 3e6, maximum injection time (IT) of 100 ms, scan range of 400–1200 m/z; (2) dd‐MS2: resolution of 17500, AGC target at 1e5, maximum IT of 50 ms, TopN of 10, normalized collision energy of 30; (3) dd settings: intensity threshold 8.0e5, minimum AGC target 4e4, dynamic exclusion for 10 s. Sequencing was based on a search against the UniProt (Swiss‐Prot) human database, containing only canonical sequences (file version from February 2025), using Proteome Discoverer 2.4 (precursor mass tolerance: 5 ppm; fragment mass tolerance: 0.02 Da, XCorr > 1.9) without enzyme specificity. No fixed modifications were selected. Oxidation of proline and methionine (indicated with “p” and “m”) was set as variable modifications. For peptide identification, the peptide rank was set to 1, with a minimum peptide length of 6 amino acids, and a maximum peptide length of 144 amino acids. Confidence levels based on Xcorr are detailed in [32]. Two separate searches were conducted for peptide identification: (1) percolator‐based validation, where only high‐confidence peptides with a false discovery rate (FDR) of 1% were considered, and (2) fixed value PSM Validator, which validated peptide‐spectrum matches (PSMs) based on predefined score thresholds using default settings. Cysteine containing peptides were eliminated. Similarly, peptides containing proline hydroxylation on other proteins that collagens were excluded. Peptides with oxidized methionine but without an unoxidized version were removed. Only peptide identifications based on at least 2 PSMs were considered.
2.6. Statistical Analysis
After transformation of the mass spectrometric spectra, the levels of peptides with known amino acid sequences were compared between different time points of sampling using the Wilcoxon rank‐sum test. Correlation analysis was based on Spearman's rank method. Correction for multiple testing was done using the Benjamini and Hochberg method [33], and the threshold of significance was an adjusted p value < 0.05.
3. Results
Fifty‐six saliva samples were collected, prepared, and analyzed using CE‐MS. Data from one sample could not be properly calibrated and was therefore excluded from further analysis.
The average number of detected peptides per sample was 3147 ± 559. There were no statistically significant differences in the number of peptides detected at different sample collection time points (t‐test, p > 0.05). The distribution of the number of peptides per time point is shown in Figure 1 (and with linked patients across the time points in Figure S2).
FIGURE 1.
Number of peptides detected per sample and collection time point.
The entire peptide panel includes 6170 peptides, defined by mass, CE‐time, and signal intensity. Figure 2 displays the compiled saliva peptide panel intensity for the different sampling time points. As shown, the peptide signature of samples collected before breakfast differs from the peptide patterns obtained after breakfast (1, 2, and 4 h). The statistical comparison of peptides detected with high frequency (those detected in > 70% of samples from a single time point) resulted in 215 ± 46 peptides that significantly differed (p < 0.05 after correction for multiple testing) between saliva samples collected before breakfast and those collected 1, 2, and 4 h after breakfast. In contrast, no significant differences in high‐frequency peptides were observed between 1, 2, and 4 h post‐breakfast samples.
FIGURE 2.
Compiled capillary electrophoresis coupled with mass spectrometry (CE‐MS) spectra of peptides in the saliva of 14 healthy volunteers. The x‐axis represents CE‐migration time [min], the y‐axis shows the log molecular mass [kDa], and the z‐axis displays the mean signal intensity expressed as peak height.
High confidence sequence information was obtained for 630 peptides. These peptides are listed in Table S1. The detected peptides are fragments from 82 different proteins (Table 1). A large fraction of these peptides represents fragments of different types of basic salivary proline‐rich protein (PRP, n = 338, 53.7%). The number of peptides identified from the 10 proteins with the highest number of peptides (Table 1) is depicted in a pie chart in Figure 3. Fragments of different PRPs (n = 18), collagens (n = 3), salivary acidic proline‐rich phosphoprotein 1/2 (PRPC, n = 12), and submaxillary gland androgen‐regulated protein 3B (n = 4) were detected in more than 90% of the analyzed saliva samples (Table S2).
TABLE 1.
Parental proteins and the number of corresponding peptides in WMS.
| Protein names | Number of peptides |
|---|---|
| Basic salivary proline‐rich protein 1 | 143 |
| Basic salivary proline‐rich protein 2 | 99 |
| Salivary acidic proline‐rich phosphoprotein 1/2 | 57 |
| Basic salivary proline‐rich protein 3 | 56 |
| Submaxillary gland androgen‐regulated protein 3B | 47 |
| Basic salivary proline‐rich protein 4 | 40 |
| Histatin‐1 | 18 |
| Collagen alpha‐1(I) chain | 11 |
| Actin | 10 |
| Collagen alpha‐5(IV) chain | 8 |
| Polymeric immunoglobulin receptor | 8 |
| Mucin‐7 | 7 |
| Collagen alpha‐2(I) chain | 6 |
| Collagen alpha‐1(IV) chain | 5 |
| Hemoglobin subunit alpha | 5 |
| Collagen alpha‐1(III) chain | 4 |
| Collagen alpha‐1(VII) chain | 4 |
| Collagen alpha‐1(X) chain | 4 |
| Collagen alpha‐1(XVI) chain | 4 |
| Hemoglobin subunit beta | 4 |
| Mucin‐19 | 4 |
| Protein S100‐A9 | 4 |
| Collagen alpha‐1(XIX) chain | 3 |
| Collagen alpha‐1(XVIII) chain | 3 |
| Collagen alpha‐1(XXII) chain | 3 |
| Collagen alpha‐1(XXVI) chain | 3 |
| Collagen alpha‐2(V) chain | 3 |
| Cystatin‐SN | 3 |
| BPI fold‐containing family A member 2 | 2 |
| Collagen alpha‐1(II) chain | 2 |
| Collagen alpha‐1(XIII) chain | 2 |
| Collagen alpha‐1(XXI) chain | 2 |
| Collagen alpha‐2(VIII) chain | 2 |
| Collagen alpha‐3(IX) chain | 2 |
| Collagen alpha‐3(V) chain | 2 |
| Histone H2A type 2‐C | 2 |
| Histone H2B type 2‐E | 2 |
| Statherin | 2 |
| Alpha‐2‐HS‐glycoprotein | 1 |
| B‐cell lymphoma/leukemia 11B | 1 |
| BRD4‐interacting chromatin‐remodeling complex‐associated protein | 1 |
| BTB/POZ domain‐containing protein 2 | 1 |
| Cell cycle and apoptosis regulator protein 2 | 1 |
| Collagen alpha‐1(IX) chain | 1 |
| Collagen alpha‐1(V) chain | 1 |
| Collagen alpha‐1(XI) chain | 1 |
| Collagen alpha‐1(XIV) chain | 1 |
| Collagen alpha‐1(XV) chain | 1 |
| Collagen alpha‐1(XVII) chain | 1 |
| Collagen alpha‐1(XXVIII) chain | 1 |
| Collagen alpha‐2(XI) chain | 1 |
| Collagen alpha‐3(IV) chain | 1 |
| Collagen alpha‐4(IV) chain | 1 |
| Collagen alpha‐5(VI) chain | 1 |
| CREB‐binding protein | 1 |
| Cytochrome P450 2F1 | 1 |
| Deleted in malignant brain tumors 1 protein | 1 |
| Dynamin‐1 | 1 |
| E3 ubiquitin‐protein ligase MARCHF11 | 1 |
| Glyceraldehyde‐3‐phosphate dehydrogenase | 1 |
| Histone H4 | 1 |
| Histone‐lysine N‐methyltransferase 2C | 1 |
| Histone‐lysine N‐methyltransferase SETD1A | 1 |
| Interferon regulatory factor 2‐binding protein 2 | 1 |
| MICAL‐like protein 2 | 1 |
| Myocyte‐specific enhancer factor 2D | 1 |
| Napsin‐A | 1 |
| PAX‐interacting protein 1 | 1 |
| Probable helicase senataxin | 1 |
| Profilin‐1 | 1 |
| Protein PEAK3 | 1 |
| Protein PRRC2A | 1 |
| Protein S100‐A8 | 1 |
| Radial spoke head protein 4 homolog A | 1 |
| Rho GTPase‐activating protein 32 | 1 |
| Scavenger receptor class A member 3 | 1 |
| Sodium/myo‐inositol cotransporter 2 | 1 |
| Synapsin‐1 | 1 |
| Uncharacterized protein KIAA2013 | 1 |
| Vesicular | 1 |
| Zinc finger protein 219 | 1 |
| Zinc finger protein 469 | 1 |
Abbreviation: WMS, whole mouth saliva.
FIGURE 3.
Pie chart representing the number of peptides derived from the 10 proteins with the highest number of corresponding peptides. The parental protein names and number of identified fragments are provided.
The average abundance of the sequenced peptides was significantly correlated (p < 0.0001) across all time points, with Spearman rank correlation coefficients ranging from 0.542 to 0.57 when comparing the mean peptide abundance before and after breakfast, and from 0.80 to 0.83 after breakfast.
When investigating longitudinal changes in peptide abundance, a consistent and significant change was observed between samples collected before breakfast and at individual time points after breakfast for 28 of the sequenced peptides, with most of them showing decreased abundance at later time points (n = 22) (Table S1). Most of these peptides were fragments of PRPs 1, 2, 3, and 4. Also decreased after breakfast were fragments of PRPC and a fragment of collagen alpha‐1(III) (COL3A1). Increased peptides included larger PRP 1 and 2 fragments (with more than 20 amino acids), a histatin‐1 fragment, and a submaxillary gland androgen‐regulated protein 3B fragment.
4. Discussion
This study reports the establishment of a reproducible protocol for the collection and CE‐MS‐based analysis of salivary peptides. The data suggest that using the Salivette collection system, sample preparation using ultrafiltration in the presence of urea and SDS (to dissociate protein‐protein interaction) followed by desalting and sampling between 1 and 4 h after breakfast, is well‐suited for the assessment of the saliva peptidome. In addition, this study provides a detailed characterization of the salivary peptidome and highlights dynamic changes in salivary peptides following food intake.
Using CE‐MS and MS/MS approaches, we identified a total of 6170 peptides, with high‐confidence sequence information obtained for 630 peptides originating from 82 different proteins. Our findings led to the establishment of a salivary reference peptide map that could serve as a basis for future studies aimed at identifying salivary peptide biomarkers in various pathological conditions.
A major fraction of the identified peptides originated from PRPs, accounting for 53.7% of the most frequently detected peptides. PRPs play a fundamental role in oral homeostasis by contributing to lubrication and bacterial adhesion [34]. Their susceptibility to proteolysis results in the release of bioactive peptides, which may modulate microbial interactions and contribute to oral immunity. The predominance of PRP‐derived peptides across all time points suggests their stable presence in saliva, yet their differential abundance before and after breakfast indicates a potential influence of dietary factors on their degradation or secretion [35].
In addition to PRPs, peptides derived from structural proteins, such as collagen alpha‐3(IX), were frequently detected, possibly reflecting ongoing tissue remodeling. Furthermore, immune‐related proteins, including BPI fold‐containing proteins and submaxillary gland androgen‐regulated proteins, were also frequently detected.
One of the key findings in this study is the significant shift in the peptide composition of saliva before and after breakfast. These results suggest that the most pronounced changes in the salivary peptidome occur within the first hour following food consumption, potentially due to enzymatic activity stimulated by mastication, salivary gland secretion, and interactions with food components. This aligns with a previous study showing that salivary proteolysis can be rapidly modulated upon food intake [34]. The general decrease in PRPs after breakfast suggests that their baseline secretion is higher in fasting conditions, possibly to maintain oral lubrication and initial microbial adhesion control. However, some PRP fragments were increased, hinting at selective processing or proteolysis post‐meal. Histatin‐11, an antimicrobial peptide [36], showed an increase at later time points. This could indicate a stimulated immune defense mechanism in response to food intake, protecting against bacterial overgrowth or foodborne microbes. Moreover, the observed decline in a COL3A1 fragment after food intake may indicate a shift in oral proteolysis or a change in salivary gland‐derived extracellular matrix components.
When comparing our dataset with previous salivary proteomics studies, we observed some overlap but also methodological differences that may account for the differences in peptide detection. The group of Leandro Xavier Neves [37] applied MS‐based peptidomics to analyze the saliva with the aim to study proteolytic events in the saliva of 79 patients and their association with oral squamous cell carcinoma prognosis. The authors identified 676 peptides using LC‐MS/MS. When comparing the peptide lists, 100 common peptides were observed in both studies. These peptides are labeled in Table S1. The use of different methods (CE vs. LC) appears to be a major cause of the observed differences. A similar observation has been reported in the case of urine, where substantial differences between LC‐MS/MS and CE‐MS/MS data were detected, even when using the same sample (including the same sample preparation) [38]. Moreover, differences in sample pretreatment, such as peptide solubility affected by varying pH solutions used for sample preparation (acid vs. basic), could also contribute to the discrepancies. Additionally, the method of saliva collection (direct collection vs. Salivette) may contribute to differences in peptide profiles.
In another study, Wazwaz et al. reported the LC‐MS/MS analysis of naturally occurring peptides in WMS [39], identifying 2852 peptides, which aligns with the number of peptides detected in our study. However, as the identified peptides from Wazwaz et al. [39] are not publicly available, no further comparisons could be performed.
Overall, our study provides valuable insights into the dynamics of the salivary peptidome and underscores the importance of dietary and enzymatic factors in shaping peptide composition. Future studies should aim to further elucidate the functional significance of these peptides, particularly in relation to host‐microbiome interactions and disease‐associated changes in saliva composition. The application of high‐throughput proteomics and multi‐omics approaches may enhance our understanding of saliva as a diagnostic biofluid and pave the way for novel biomarker discoveries in oral and systemic diseases [13].
Conflicts of Interest
Harald Mischak is the co‐founder and co‐owner of Mosaiques‐Diagnostiques (Hannover, Germany). Justyna Siwy, Agnieszka Latosinska, Ioanna K. Mina, and Marika Mokou are employees of Mosaiques‐Diagnostics.
Supporting information
Supporting Information
Supporting Information
Supporting Information
Acknowledgments
This work was funded by the Research Fund (FoU) at Skaraborg Hospital, Skövde, Sweden (VGSKAS‐974687).
Funding: This work was funded by the Research Fund (FoU) at Skaraborg Hospital, Skövde, Sweden (VGSKAS‐974687).
Data Availability Statement
The raw data are deposited in Zenodo under the accession https://doi.org/10.5281/Zenodo.11076567
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Data Availability Statement
The raw data are deposited in Zenodo under the accession https://doi.org/10.5281/Zenodo.11076567