Comparative proteomic analysis of saliva and salivary stones in sialolithiasis

Comparative proteomic analysis of saliva and salivary stones in sialolithiasis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparative proteomic analysis of saliva and salivary stones in sialolithiasis Natalia Musiał, Inez Mruk, Dmitry Tretiakow, Andrzej Skorek, Konrad Szydłowski, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8967608/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract The proteins associated with poorly described mechanism of sialolithiasis pathogenesis were previously described, but to increase credibility of these results and to discover new biomarkers of this disease it would be beneficial to verify the validity of optimized protocols during performing the quantitative analysis to establish the most reasonable reference sample. Previously established protocols were used to perform optimal protein extraction and digestion in saliva and salivary stone samples. Based on the DDA spectra the well-developed spectra library was created and then the DIA spectra were used to conduct relative quantitative proteomic analysis of saliva and sialoliths. The optimized workflows allowed to quantify the proteins in saliva and salivary stone samples. After statistical analysis it was possible to compare protein profiles of different saliva samples and sialolith samples, depending on the chosen reference sample. This study verified the applicability of saliva as reference sample in quantitative proteomic analysis of sialoliths, but at the same time no differences between saliva from healthy donors and saliva from patients with sialolithiasis were detected. It was possible to identify high confident proteins associated with pathological calcification leading to sialoliths formation. Biological sciences/Biochemistry Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Proteomics Saliva Salivary stones Biomarkers Biocalcification Sialolithiasis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 INTRODUCTION Sialolithiasis is a relatively uncommon and underrecognized disorder characterized by the formation of calcified deposits, known as sialoliths or salivary stones, which occur in approximately 1–2% of the population. These stones develop within the salivary ducts or glands, most frequently in the submandibular glands, and less often in the parotid or sublingual glands. Clinical complications arise when a sialolith enlarges sufficiently to obstruct salivary flow, leading to pain—particularly during meals—swelling of the affected gland, and occasionally fever. The condition is often accompanied by bacterial infection and purulent inflammation of glandular tissues. At present, surgical removal remains the only effective treatment option [ 1 – 5 ]. Based on the spectroscopic studies there was proposed classification of salivary stones. They were divided into three types: calcified (CAL), lipid (LIP), and mixed (MIX). According to results of spectroscopic analysis, these established sialolith types differ from each other in their developmental pathways, when the balance between calcium and lipid components becomes disrupted [ 6 ]. The mechanism of biocalcification underlying sialolith formation remains poorly understood. Sialoliths exhibit complex structural organization, typically comprising an inner core surrounded by concentric layers. Depending on the stone type, these layers may be primarily inorganic—with a well-characterized structure—or organic, containing incompletely defined constituents such as proteins, lipids, and bacteria. Both commensal and pathogenic microorganisms have been identified within these layers, supporting the hypothesis that bacteria may play a role in biocalcification and stone development [ 7 – 15 ]. Conversely, other studies have shown that epithelial cells of the salivary glands secrete peptides and proteins involved in digestion, lubrication, mineralization, tissue protection, buffering, and antimicrobial defense, so any changes in their levels may be crucial for development of sialolithiasis [ 16 – 18 ]. One study found that neutrophil extracellular trap (NET) formation plays a crucial role in sialolith development. NETs occur when neutrophils release chromatin and granular proteins found in saliva in the presence of bacteria. NETs often aggregate into structures called aggNETs, which act as "glue," binding proteins and calcium crystals to form macroscopic sialoliths. NETs have been identified as contributors to deposit formation in other organs as well. Targeting NET formation could be a promising strategy to prevent salivary stone development [ 19 – 27 ]. Our working hypothesis posits that concentration of salivary or infection-derived proteins influences the calcium–lipid equilibrium, promoting sialolith formation. Identifying potential biomarkers could therefore provide insight into the biocalcification mechanisms driving this condition. Findings from our previous studies have highlighted several promising biomarker candidates, consistent with the current classification of salivary stones. Enrichment analysis confirmed, that these proteins are mainly associated to immune response, mainly because of the presence of pathogens, such as bacteria. They were also responsible for regulation of calcium or lipids balance and maintaining the structure of extracellular regions structure. To validate these potential markers, a comparative proteomic analysis of sialoliths and saliva from affected patients versus saliva from healthy individuals is required. Despite saliva’s inherent instability, such comparisons could yield clinically relevant information, revealing proteins specifically associated with sialolithiasis [ 28 , 29 ]. There is possibility to detect many biomarkers in saliva in the terms of oral diseases (lichen planus, periodontitis, and primary Sjögren’s syndrome): interleukins (IL1, IL4, IL6, IL8, IL10), C-reactive protein (CRP), matrix metalloproteinases (MMP8, MMP9), pro-epidermal growth factor (EGF) and parotid secretory protein (PSP) [ 30 ]. Saliva is also a valuable material for detecting biomarkers associated with various inflammatory and systemic diseases, including pneumonia (CRP), bowel disease (interleukins), diabetes (MMPs, melatonin, lysozyme, glucose), and Cushing’s disease (cortisol) [ 30 – 34 ]. It can also be used to diagnose infectious diseases—for example, by detecting antibodies against human immunodeficiency virus (HIV) [ 35 ]. Mass spectrometry (MS) coupled with liquid chromatography (LC) enables the high-throughput identification and quantification of proteins within a single analytical run. However, achieving reliable results depends on optimized protein extraction, digestion, and MS acquisition protocols. Given the limited availability of clinical material, optimization is essential for reproducible proteomic outcomes. In our last study (Musiał et al. Optimization of protein extraction and digestion workflows for proteomic analysis of saliva, salivary stones and kidney stones , Frontiers in Molecular Biosciences, 2026, accepted for publication) we checked the validity of optimized protocols and we confirmed, that it is possible to detect some repeated proteins in saliva and salivary stones. Despite the small sample size, our findings point the potential of optimized proteomics in enhancing biomarker discovery and the understanding of pathological calcification in clinical background. The aim of this study was validation of saliva as reference sample during protein profiling of sialoliths. In the first place the differences between saliva from healthy donors and saliva from patients with sialolithiasis should be identified, including the presence of bacterial proteins. Thanks to that the establishment of sialoliths proteome would be more reliable, because of more repeatable protein composition of saliva than proteome of pooled sialoliths sample. Important part is also validation of previously established protein profiles of different types of salivary stones. RESULTS The proteomic analysis was performed on a set of clinical samples including 17 sialoliths and 12 saliva samples in total. Specific data are presented in the Table 1 . The pictures of processed salivary stone samples are presented in the Supplementary File 1 . All of the clinical samples were processed according to the FASP methodology, but sonication enhancement was also applied – after the digestion performed based on 2 different protocols (standard FASP and sonication-aided FASP) equally amounts of obtained peptides were combined for each sample and prepared for MS analysis. During this analysis both DDA (1 replicate) and DIA (3 replicates) spectra were registered for each sample. To create spectral library, the spectra of 4 groups of samples were used: single DDA spectra for each salivary stone and saliva sample described in this work; single DDA spectra for each salivary stone sample described and recorded during our previous work [ 29 ]; DDA spectra for pooled sialolith protein fractions after separation in gel (SageELF) and digested according to FASP protocol, described and recorded during our previous work [ 29 ]; DDA spectra for peptide fractions after FASP digestion and chromatographic separation in alkaline pH, described and recorded during our previous work [ 29 ]. Thanks to that we were able to build the spectral library containing 1428 proteins. After the manual verification of the spectral library we were able to quantify 141 proteins. Data processing and statistical analysis allowed us to identify up-regulated and down-regulated proteins, relatively to chosen control group. Table 1 Sialolith and saliva samples summary. patients with sialolithiasis # sialolith samples # saliva samples 17 - 6 with saliva samples (1–6) - 11 without saliva samples (A-K) 6 (1–6) healthy donors 0 6 (A-F) Principal component analysis shows the grouping of the samples (Fig. 1 ). PCA clearly shows, that the samples of saliva from healthy donors and saliva from patients with sialolithiasis are grouped together, suggesting the similarity of proteins composition, while proteomes of salivary stones are more variable. Detection of bacterial proteins in sialoliths, salivary from patients with sialolithiasis and saliva from healthy donors First, we examined the DDA spectra of all clinical samples to determine whether bacterial proteins could be detected. Raw data were processed against a bacterial protein database using an oral cavity keyword filter. Proteins from multiple bacterial species were identified: 29 species in sialolith samples, 7 species in saliva from patients with sialolithiasis, and 21 species in saliva from healthy donors. The identified bacterial species are listed in Table 2 . The overlap of bacterial species among the compared sample groups is illustrated using a Venn diagram (Fig. 2 ). Table 2 List of identified bacterial species identified among the compared sample groups. salivary stones saliva from patients with sialolithiasis saliva from healthy donors Actinomyces bowdenii Arachnia propionica Bacteroides heparinolyticus Bifidobacterium dentium Capnocytophaga catalasegens Capnocytophaga felis Conchiformibius kuhniae Conchiformibius steedae Dentiradicibacter hellwigii Fusobacterium canifelinum Fusobacterium nucleatum Fusobacterium pseudoperiodonticum Lactobacillus crispatus Parvimonas micra Rodentibacter pneumotropicus Schaalia canis Streptococcus dentalis Streptococcus gingivalis Streptococcus halitosis Streptococcus lingualis Streptococcus minor Streptococcus salivarius Streptococcus sanguinis Streptococcus troglodytae Tannerella forsythia Uruburuella suis Uruburuella testudinis Veillonella nakazawae Vitreoscilla massiliensis Actinomyces bowdenii Conchiformibius steedae Fusobacterium canifelinum Parvimonas micra Streptococcus minor Streptococcus salivarius Streptococcus troglodytae Actinomyces bowdenii Bacteroides heparinolyticus Bifidobacterium dentium Capnocytophaga felis Conchiformibius kuhniae Conchiformibius steedae Fusobacterium canifelinum Parvimonas micra Schaalia canis Streptococcus dentalis Streptococcus gingivalis Streptococcus halitosis Streptococcus lingualis Streptococcus minor Streptococcus salivarius Streptococcus sanguinis Streptococcus troglodytae Tannerella forsythia Veillonella nakazawae Vitreoscilla massiliensis Vitreoscilla stercoraria Quantitative analysis of sialoliths protein composition, taking into account the defined protein profile of different types of salivary stones In the terms of quantitative analysis, in the first place we performed exactly the same analysis as in our previous paper [ 29 ] – based on the DIA spectra and constructed spectral library the relative quantitative proteomic analysis was done. We were able to identify statistically significant (p-value < 0,05) up-regulated (log 2 FC ≥ 0,6) and down-regulated (log 2 FC ≤ -0,6) proteins in sialoliths (ratio between each sialolith sample and pooled sample of all sialoliths samples). Table 3 shows detailed information how many proteins were quantified for each tested sample. Besides, Table S1 (Supplementary File 2) present comprehensive results of quantitative analysis. Based on these results we were trying to identify each sialolith sample as a representative sample of one of the three known types of salivary stones: CAL, LIP or MIX. To achieve that, we compared the sets of proteins established during our previous research [ 29 ] as standard protein profile of each type and the sets of proteins detected during this research. The number of overlapped between these two sets proteins is presented in Table 3 . What is more, we also checked the unique proteins for each sialolith type. Table 3 All tested salivary stone samples summary detailed data. sample ID Quantify proteomic analysis (control: salivary stones (pooled); p-value < 0,05) # proteins overlapped with proteins defined for different types of sialoliths (# unique proteins for that type of sialoliths) ANOVA p-value for comparisons of control samples for group of overlapped proteins quantified by calculating the ratio between sialolith sample and control sample: t-test p-value for pairwise comparisons of control samples for group of overlapped proteins quantified by calculating the ratio between sialolith sample and control sample : # up-regulated proteins (log 2 FC ≥ 0,6) # down-regulated proteins (log 2 FC ≤ -0,6) CAL LIP MIX salivary stones (pooled) vs saliva from healthy donors (pooled) salivary stones (pooled) vs saliva from patients with sialolithiasis (pooled) saliva from healthy donors (pooled) vs saliva from patients with sialolithiasis (pooled) #1 3 89 25 (15) 13 (3) 13 (0) 0,60 0,33 0,85 0,44 #2 3 99 31 (18) 14 (2) 18 (1) 0,89 0,84 0,64 0,79 #3 1 77 24 (14) 12 (2) 13 (0) 0,79 0,86 0,52 0,63 #4 9 69 28 (17) 12 (1) 14 (0) 0,92 0,98 0,74 0,72 #5 8 55 21 (14) 8 (1) 9 (0) 0,93 0,89 0,83 0,72 #6 4 34 10 (7) 4 (1) 4 (0) 0,97 0,90 0,92 0,82 #A 10 72 29 (16) 12 (2) 16 (1) 0,95 0,76 0,94 0,83 #B 6 65 19 (10) 8 (3) 11 (1) 0,94 0,97 0,77 0,75 #C 7 35 15 (11) 5 (1) 5 (1) 0,93 0,72 0,97 0,76 #D 5 94 31 (14) 16 (2) 20 (0) 0,92 0,84 0,69 0,84 #E 5 124 38 (23) 18 (3) 20 (1) 0,78 0,66 0,48 0,80 #F 3 70 18 (10) 11 (3) 10 (0) 0,69 0,43 0,51 0,86 #G 6 119 35 (18) 16 (3) 20 (1) 0,86 0,71 0,59 0,86 #H 5 105 34 (18) 15 (1) 20 (1) 0,79 0,92 0,53 0,6 #I 9 86 27 (17) 14 (2) 13 (0) 0,93 0,92 0,80 0,72 #J 2 103 28 (17) 14 (2) 15 (1) 0,84 0,80 0,73 0,56 #K 9 108 30 (20) 11 (1) 13 (0) 0,95 1,00 0,78 0,77 Differences between sialoliths and saliva collected from different patients Next step was proteomic analysis of saliva and sialolith samples collected from each patients. We had six of these sets of paired samples. Again, we performed relative quantitative proteomic analysis to detect statistically significant (p-value < 0,05) up-regulated (log 2 FC ≥ 0,6) and down-regulated (log 2 FC ≤ -0,6) proteins in sialoliths, but in this case the referent samples were saliva samples from respective patients. Table 4 shows the number of quantified protein in each sialolith sample. The detailed data are shown in the Table S1 (Supplementary File 3) . Table 4 A table with the number of up-regulated and down-regulated proteins for each sialolith sample (control: respective saliva samples collected from the same patient), which are statistically significant (p-value < 0,05) and the log 2 FC ≥ 0,6 for up-regulated proteins and log 2 FC ≤ -0,6 for down regulated proteins. sample ID #1 #2 #3 #4 #5 #6 # up-regulated proteins (log 2 FC ≥ 0,6) 4 2 0 8 7 8 # down-regulated proteins (log 2 FC ≤ -0,6) 95 104 85 74 71 47 To check, if the protein profile is similar for each salivary stone sample, we took the proteins common for all samples (23 proteins) and presented the distribution of their log 2 FC values as the box plots (Fig. 3 A). ANOVA p-value was calculated (p-value = 0,003872). What is more, the t-test p-values for pairwise comparisons of quantitative data of common proteins among tested salivary stone samples were also calculated and they are presented as correlation plot (Fig. 3 B). Differences between saliva from patients with sialolithiasis and saliva from healthy donors In the first place we wanted to check if there are some proteins, which level is statistically different between saliva from healthy donors and saliva from patients with sialolithiasis. To verify that with high confidence we performed 3 different comparisons: 1) saliva from patients with sialolithiasis (median value of all samples) vs saliva from healthy donors (median value of all samples); 2) saliva from patients with sialolithiasis (pooled) vs saliva from healthy donors (pooled); 3) each saliva from patients with sialolithiasis vs saliva from healthy donors (pooled). For each comparison the t-test was performed, but, based on p-values, there are very few statistically different proteins between these two types of saliva samples (p-value < 0,05; up-regulated proteins: log 2 FC ≥ 0,6; down-regulated proteins: log 2 FC ≤ -0,6). Comparing median values of all samples between saliva from patients with sialolithiasis and saliva from healthy donor (comparison 1.), there is only 1 statistically significant protein: Salivary acidic proline-rich phosphoprotein ½ (PRH1; log 2 FC = -1,00). For comparison 2., where pooled saliva from patients with sialolithiasis was compared with pooled saliva from healthy donors, there are 2 statistically different proteins: Hemoglobin subunit beta (HBB; log 2 FC = -1,34) and Junction plakoglobin (JUP; log 2 FC = -6,22). In the case of separately compared saliva samples, where the control was pooled saliva from healthy donors (comparison 3.), only for saliva sample #4 statistically significant proteins were detected: Hemoglobin subunit beta (HBB; log 2 FC = -1,84) and Submaxillary gland androgen-regulated protein 3B (SMR3B; log 2 FC = -3,17). Comparison of protein profile of sialoliths depending on the selected control sample Last part was comparison the proteomes of salivary stones established depending on the selected reference sample. First, we performed the same quantitative proteomic analysis as previously, where control sample was pooled sample of all sialoliths samples, but here we chose the saliva collected from healthy donors (pooled) and saliva collected from patients with sialolithiasis (pooled) as reference samples. The statistically significant (p-value < 0,05) up-regulated (log2FC ≥ 0,6) and down-regulated (log2FC ≤ -0,6) proteins were identified. The detailed data are presented in the Table S1 (Supplementary File 4) and Table S1 (Supplementary File 5) , respectively. Then we selected proteins, which were quantified in more that 50% of sialolith samples for each comparison – there were 92 proteins for analysis with pooled salivary stone samples as reference, 102 proteins for analysis with pooled saliva samples from healthy donors as reference and 96 proteins for analysis with pooled saliva samples from patients with sialolithiasis as reference. The overlapping of these set of proteins is presented on the Venn diagram ( Figure 4A ). There are 76 common proteins. Next, the distribution of median log 2 FC values of these proteins is shown as box plots ( Figure 4B ). ANOVA and t-test were conducted to calculate p-values an to check if there are statistically significant differences between the groups. The p-values for the overlapped sets of proteins are shown on the Figure 4B , but the ANOVA and t-tests were also conducted for each separate sialolith sample and the p-values for these comparisons are shown in the Table 3 . DISCUSSION Describing sialolithiasis, there is still no confirmed cause or factor leading to biocalcification process and formation of stones in salivary glands or salivary ducts. During our previous studies we were trying to identify potential protein biomarkers, which are crucial in this pathological state. To perform this analysis we were comparing the sialolith samples between each other, but we wanted to find proper control samples group. We decided to use saliva as control sample – during our previous work (Musiał et al. Optimization of protein extraction and digestion workflows for proteomic analysis of saliva, salivary stones and kidney stones, Frontiers in Molecular Biosciences, 2026, accepted for publication) we checked the applicability of this comparison and we found some set of common proteins. However, it was qualitative analysis based on DDA spectra, that is why we wanted to performed quantitative analysis based on DIA spectra. In this way it is possible to check the relative level of proteins, comparing salivary stone samples and saliva samples. The disturbances of proteins level may indicate the connection between their functions and biocalcification mechanism leading to the sialoliths formation. In the first place we wanted to check if it is possible to identify some bacterial proteins in our clinical samples. Processing the raw data against protein database including bacteria species associated with oral cavity we were able to detect bacterial proteins present in sialoliths, saliva from patients with sialolithiasis and saliva from healthy donors. Table 2 presents the bacterial species for which proteins were identified in the analyzed samples. The highest number of species was detected in sialoliths (29 species), followed by saliva from healthy donors (21 species), while the lowest number was observed in saliva from patients with sialolithiasis (7 species). Importantly, all species detected in saliva from patients with sialolithiasis were also present in the other sample types. All identified bacterial species are considered typical members of the oral cavity microbiota [ 36 – 38 ]. Many of the identified bacteria belong to opportunistic pathogens, particularly species of Actinomyces , Capnocytophaga , Streptococcus , and Fusobacterium [ 39 – 43 ], whereas Tannerella forsythia is considered a true pathogen [ 44 ]. We compared the bacterial species detected in salivary stones in the present study with those identified in our previous research [ 29 ]. Four identical species were detected in both studies: Fusobacterium canifelinum , Fusobacterium nucleatum , Fusobacterium pseudoperiodonticum , and Tannerella forsythia . At the genus level, representatives of the same four genera - Actinomyces , Fusobacterium , Streptococcus , and Tannerella - were identified. These genera comprise bacteria that are typical inhabitants of the oral cavity, predominantly acting as opportunistic pathogens. A previous study analyzed the microbiome of sialoliths, as well as saliva from patients with sialolithiasis and from healthy donors, at the transcriptomic level [ 45 ]. The authors aimed to identify the bacterial genera present in the analyzed samples. We compared their findings with the data obtained in the present study, and the results are illustrated using Venn diagrams (Fig. 5 ). The sets of bacteria detected by proteomic analysis were noticeably less diverse, most likely due to the smaller number of analyzed samples (proteomic analysis: 6 affected patients; transcriptomic analysis: 27 affected patients). Nevertheless, several bacterial genus were common to both datasets. The most frequently overlapping genera included Actinomyces , Capnocytophaga , Fusobacterium , Streptococcus , and Tannerella . Comparative analysis of proteomic and transcriptomic data indicates that the salivary microbiome of healthy donors and patients with sialolithiasis is highly similar. In contrast, the microbiome of sialoliths shows differences, although some bacterial genus are shared. This overlap suggests interactions between the salivary stone environment and saliva, which may contribute to pathological calcification and, consequently, to deposit formation in the salivary glands or ducts. First part of quantitative part of this research was conducting the proteomic analysis of the sialoliths compound in the exactly the same way as during our previous research, where our control was pooled sample of all tested salivary stone samples. Table 3 presents the exact numbers of detected in each sample up-regulated and down-regulated statistically significant proteins. However, we could not expect exactly the same results, because of the protein composition of pooled sample – there was set of different sialolith samples, so the pooled control sample was also different. We wanted to check if we were able to detect some repeating proteins, especially taking into account the division of the sialoliths into 3 different groups (CAL, LIP an MIX). During previous research the set of proteins was in the first place analyzed using spectroscopic methods and based on the obtained results the stone samples were divided into 3 groups according to the proposed classification [ 6 ]. We compared the proteins quantified during this study and sets of proteins quantified and established for each sialolith type during previous study. We were analysing 3 sets of proteins: CAL – 55 proteins, LIP – 22 proteins, and MIX – 26 proteins. We were able to detect in all of the sialolith samples some part of proteins, which were earlier established for each type of stone. What is more, in the case of CAL and LIP stones the unique for sialolith type proteins were identified in each sample. About MIX salivary stones, there was only 1 unique protein - Fibrinogen alpha chain (FGA), associated with activity of neutrophils and formation of NETs [ 46 , 47 ]. This protein was found in 8 out of 17 samples. To discuss the overall protein profile of sialoliths we selected the set of 92 proteins, which were identified in at least 50% of tested samples. Based on that we generated the network of interactions between these proteins (Fig. 6 ). Border color of the nodes indicates the type of regulation of these proteins. Most of them (78 proteins) were down-regulated among all the samples. Only 1 protein – Eosinophil cationic protein (RNASE3) – was up-regulated among all the samples. This protein is ribonuclease with ability to cytotoxicity and binding of heparin, used during inflammation state, also in saliva [ 48 – 50 ]. There were also 13 proteins, which level of regulation was varied among the samples. Analysing the sialoliths classification, we identified 26 proteins defined as proteome of CAL salivary stones, 13 proteins – LIP stones, and 14 proteins – MIX sialoliths. The most important are proteins detected in each type of salivary stones and we were able to found 7 of these proteins: Eosinophil cationic protein (RNASE3), Hemoglobin subunit beta (HBB), Neutrophil elastase (ELANE), Cystatin-S (CST4), Cystatin-SN (CST1), Statherin (STATH) and Immunoglobulin gamma-1 heavy chain (P0DOX5). Neutrophil elastase (ELANE) is secreted during inflammation by neutrophils and with the ability to bind the DNA this proteins is associated with formation of neutrophil cellular traps [ 51 ]. Cystatin-S (CST4) and Cystatin-SN (CST1) are responsible for binding the calcium also in the saliva. It may influence the changes in the balance of calcium, leading to the biocalcification process and formation of sialoliths [ 52 ]. Next protein is Statherin (STATH), small protein, which theoretically should be washed-out during digestion procedure performed on the filters with membrane. It is another example of protein influencing the saliva composition, because by inhibition of calcium phosphate salts precipitation, the role of this protein is stabilization of saliva [ 53 ]. Presence of Immunoglobulin gamma-1 heavy chain (P0DOX5) confirms the role of immune system in the formation of salivary stones. It was possible to identify repeating proteins, but it is impossible to divided tested salivary stone samples into one of three established groups based on the protein profile. It depends on the chosen control sample – during previous and present studies the pooled samples, made by mixing all tested samples, was selected as reference, but the set of clinical samples was different, so the protein composition of reference sample was different and, as a consequence, the results of proteomic analysis were varied. To establish constant protein profile of each sialolith type we need another control sample with relative stable and repeatable protein composition. That is why we decided to validate saliva as reference, both collected from patients with sialolithiasis and, more importantly, from healthy donors. Despite a different set of proteins that have been identified during this study, performed enrichment analysis (Fig. 7 ) shows, that the roles and functions of quantified proteins are similar to data obtained earlier [ 28 , 29 ]. According to Biological Process Gene Ontology database most of the proteins are associated with immune system processes and defense response to presence of bacteria. It may confirm, that the bacteria can cause formation of sialoliths. What is more, quantified proteins are also responsible for causing the changes in the lipids homeostasis. There are enrichment terms detected, which may indicate this: Regulation of peptidase activity (17 proteins), Negative regulation of endopeptidase activity (16 proteins), Response to lipid (13 proteins) and Cellular response to lipid (9 proteins) [ 54 ]. Besides, 7 proteins are connected to Ossification term. This process has been previously associated to sialoliths formation, because they are formed during calcification process, which is precursor to ossification (bone formation) [ 55 ]. In the case of Molecular Function Gene Ontology database, Endopeptidase inhibitor activity (14 proteins) term was detected, pointing on the process of altering the level of lipids. On the other hand, the balance of calcium level is also disturbed – the Heparin binding (7 proteins) and Calcium-dependent protein binding (5 proteins) terms were found [ 56 ]. Discussing detected Cellular Component Gene Ontology terms, most of them are associated to extracellular region and it may show the role of neutrophil extracellular traps in the biocalcification process. There was also Cornified envelope (4 proteins) term detected, referring to tough and insoluble protein structure, which may be formed in oral mucosa. Lipids play the key role during this process [ 57 – 59 ]. KEGG and Reactome databases also show, that the immune response and extracellular components are crucial during sialoliths formation. Next part of this study was checking the pattern of relative level of proteins in sialolith compared to saliva, collected from the same patients with sialoliths. Table 4 shows the number of up-regulated and down-regulated statistically significant proteins, detected in each salivary stone sample. Most of the proteins are down-regulated. We selected the repeating in each sample proteins – there were 23 of these proteins. Based on the results presented as box plots and statistical tests (ANOVA and t-test), there is no common pattern of distribution of log 2 FC values – ANOVA p-value = 0,003872, so it means that there is a statistically significant difference between the means of at least two groups. T-test p-values indicate, that there are statistically significant differences (p-value < 0,05) in distribution of log 2 FC values between sialoliths #1 and #6, #2 and #5, #2 and #6, #3 and #5, #3 and #6. However, all of the overlapped proteins are down-regulated in all tested samples. We prepared the network of the protein-protein interactions for the common set (Fig. 8 A). There are 2 proteins, which were described earlier and they were previously identified as a part of proteome of all 3 types of sialoliths (CAL, LIP and MIX): Neutrophil elastase (ELANE) and Cystatin-S (CST4). Neutrophil gelatinase-associated lipocalin (LCN2) and Albumin (ALB) were present both in CAL and MIX sialoliths. LCN2 plays the key role in the transport of hydrophobic molecules, so any changes in the level of this protein may cause the lipids imbalance. Neutrophil gelatinase-associated lipocalin was also identified as a part of NETs [ 60 ]. On the other hand, the changes in the Albumin (ALB) level may also be reason of biocalcification process because the ability to binding of calcium ions [ 61 ]. Haemoglobin subunit alpha (HBA2) was found both in LIP and MIX salivary stones. There were also 4 proteins identified as a part of protein profile of only CAL stones: Protein S100-A12 (S100A12), Keratin, type II cytoskeletal 2 epidermal (KRT2), Vitronectin (VTN) and Immunoglobulin J chain (JCHAIN). S100A12 regulates immune response and in the presence of bacteria is key member of NETs. This proteins binds also calcium and, forming calprotectin complex, shows antimicrobial properties. Protein S100-A12 and Vitronectin (VTN) were also classified as markers of periodontitis [ 62 – 68 ]. The presence of Immunoglobulin J chain (JCHAIN) indicates the activation of immune system, for example because of the presence of bacteria. The enrichment analysis (Fig. 8 B) of this set of proteins and Biological Process Gene Ontology database shows, that most of the proteins are responsible for activation and regulation of immune system, most probably in the presence of bacteria, what may be a proof of the influence of microbes on the calcification process leading to the formation of the sialoliths. The detection of Heparin binding (4 proteins) Molecular Function Gene Ontology term points on the disturbances of calcium homeostasis. The role of neutrophil extracellular traps in the sialolithiasis may be confirmed by quantification of proteins associated with extracellular components. The analysis of Reactome database complements the above conclusions. Next, we wanted to check the differences in the protein profile of saliva collected from healthy donors and patients with sialolithiasis. The aim was to verify the applicability of saliva, especially the saliva collected from healthy person, as proper control to perform quantitative proteomic analysis of sialoliths. First look at the Principal Component Analysis (Fig. 1 ) may suggest that there aren’t many differences between these two groups of samples – all tested saliva samples are grouped together. To conduct detailed analysis, we performed 3 different comparisons: saliva from patients with sialolithiasis (median value of all samples) vs saliva from healthy donors (median value of all samples), saliva from patients with sialolithiasis (pooled) vs saliva from healthy donors (pooled) and each saliva from patients with sialolithiasis vs saliva from healthy donors (pooled). Unfortunately, according to statistical analysis, there are almost no statistically different proteins between these 2 types of saliva. There are only 4 statistically significant proteins: Salivary acidic proline-rich phosphoprotein ½ (PRH1), Hemoglobin subunit beta (HBB; quantified in 2 analysis), Junction plakoglobin (JUP) and Submaxillary gland androgen-regulated protein 3B (SMR3B). Salivary acidic proline-rich phosphoprotein ½ (PRH1) is responsible for protection and repairing of dental enamel. This protein inhibits growth of crystals, regulates balance of calcium phosphate and has ability to binding to the bacterial cells [ 69 – 71 ] and this may refer to the hypothesis about role of imbalance of calcium and presence of bacteria in biocalcification process. Junction plakoglobin (JUP) plays the key role in cell-cell adhesion as a part of adherens junctions and desmosomes, it is also responsible for cell signaling [ 72 ]. Down-regulation of JUP in saliva was confirmed in the case of autoimmune liver diseases [ 73 ]. Disturbed regulation of Submaxillary gland androgen-regulated protein 3B (SMR3B) may influence the level of lipids by inhibition of endopeptidase activity [ 74 ]. Final step was comparison of protein profiles of salivary stones depending on the selected reference sample. When comparing, we took into account 3 control samples: salivary stones (pooled), saliva from healthy donors (pooled) and saliva from patients with sialolithiasis (pooled). The distribution of median log 2 FC of 76 common proteins, presented as box plots (Fig. 4 B), suggests, that the proteomes of sialoliths are similar, regardless of the selected reference sample. To confirm that, the statistical test were performed: ANOVA, to determine if there are any statistically significant differences between the means of three compared groups, and t-test, to determine if there is a statistically significant difference between the means of two groups. ANOVA p-value = 0,9741 indicates that there is no significant difference between the means of the groups being compared. Additionally, t-test p-values for the for pairwise comparisons (0,826; 0,931; 0,886) also indicate that there are no significant differences between the means of 2 compared groups. The ANOVA and t-test were also performed for each separate sialolith sample (Table 3 ) and the fact that each p-value is higher than 0,05 indicates that there are no significant differences between compared reference samples. We prepared the network of interactions between the proteins from analysed set (Fig. 9 ). There was 1 up-regulated among all of the samples (regardless of the selected reference sample) protein - Eosinophil cationic protein (RNASE3). 61 proteins were down-regulated and 14 had varied type of regulation among the samples. Taking into account the established earlier classification of sialoliths and their protein profiles, we quantified 7 proteins present in all stone types (CAL, LIP and MIX). These are the proteins: Eosinophil cationic protein (RNASE3), Cystatin-S (CST4), Cystatin-SN (CST1), Statherin (STATH), Neutrophil elastase (ELANE), Immunoglobulin gamma-1 heavy chain (P0DOX5) and Hemoglobin subunit beta (HBB). All of the proteins were described above. The results of the functional analysis of this set of proteins give analogous conclusions (Fig. 10 ). Taking into account the Gene Ontology (Biological Process, Molecular Function and Cellular Component), KEGG and Reactome databases, we can assume, that the quantified proteins are mainly associated with activation of immune system, defence mechanisms in the presence of bacteria, altering the levels of calcium and lipids and regulation of extracellular region structure. Any changes in the homeostasis of these processes may be the reason of the biocalcification process and formation of sialoliths. To select the potential biomarkers of sialolithiasis, which are involved in pathological biocalcification leading to formation of deposits in salivary glands or salivary ducts, we selected the most frequent proteins among the quantified proteins during the above analysis and established as members of the proteomes of different sialoliths types [ 29 ]. We compared 4 sets of proteins quantified during this analysis: proteins identified in more that 50% of sialolith samples with 1) pooled sialolith samples, 2) pooled saliva from healthy donors, 3) pooled saliva from patients with sialolithiasis as reference samples and 4) common for all tested sialolith samples proteins with respective saliva samples collected from the same patient as reference samples. Proteins, which were overlapped between at least 2 compared sets were then compared to proteins identified during our previous research. In this way we got 33 proteins and the network of their interactions was prepared (Fig. 11 A). Among considered set, there were 8 proteins identified previously in CAL, LIP and MIX sialoliths; 5 – CAL and MIX; 3 – LIP and MIX; 15 – only CAL; 2 – only LIP. Discussing the type of regulation of protein, only 1 protein was up-regulated among all of the samples, 24 proteins were down-regulated and 8 proteins had varied type of regulation. Most of the proteins were described above. Alpha-amylase 1A (AMY1A) has ability to binding of calcium, especially in saliva [ 75 ]. Zinc-alph-2-glycoprotin (AZGP1) degrades of lipids [ 76 ]. Lactotransferrin (LTF) was described as a crucial part in the NETs formation. It has antimicrobial activity and it depends on the extracellular cation concentration. LTF was also reported as a factor responsible for decreasing inflammatory processes, bacterial growth and biofilm development in saliva. Lactotransferrin is also established as a biomarker of salivary gland pathological states [ 60 , 77 – 81 ]. Example of protein with affinity to calcium ions is Matrix Gla protein (MGP) [ 82 ]. Mucin-7 (MUC7) takes part in the inflammatory processes, especially in saliva [ 83 , 84 ]. Leukocyte elastase inhibitor (SERPINB1) is another protein responsible for immune response in the presence of pathogens [ 85 ] There is also protein, Stomatin (STOM), which regulates the activity of ion channels in membranes [ 86 ]. Annexin A5 (ANXA5), as calcium-dependent protein, binds phospholipids and expose them on the cell surface during apoptosis [ 87 ]. Another protein playing crucial role in the activation of immune system in the presence of bacteria is Bactericidal permeability-increasing protein (BPI) [ 88 , 89 ]. Cathelicidin antimicrobial peptide (CAMP) and Immunoglobulin lambda constant 2 (IGLC2) are also responsible for immune response in saliva [ 90 – 92 ]. Lactoperoxidase (LPO) is secreted by the salivary glands or epithelial cells in the oral cavity and it produces antimicrobial agents by combining thiocyanate and hydrogen peroxide [ 93 ]. The final enrichment analysis confirmed previous conclusions (Fig. 11 B). Analysing Biological Process Gene Ontology terms we can conclude, that the quantified and altered proteins, found in sialolithiasis, are mainly associated with the activity of immune system, what the most often depends on the presence of pathogens, such as bacteria. Focusing on Molecular Function Gene Ontology database, the main conclusion is fact, that in the case of pathological biocalcification the activity of endopeptidases may be disturbed and it may influence the balance of lipids. According to Cellular Component Gene Ontology terms, described proteins are also connected to extracellular regions – because of the presence of bacteria and during immune response, the neutrophil extracellular traps are formed. These structures are also crucial factors for formation of salivary stones, according to one of the theories. CONCLUSIONS Identification of bacterial proteins in both saliva and sialolith samples indicates that bacteria may contribute to the formation of salivary deposits. Although the overall salivary microbiome of patients with sialolithiasis is largely similar to that of healthy donors, a broader diversity of bacterial species is detected within sialoliths. Notably, several species are shared between sialoliths and saliva from both patient and control groups, suggesting close interactions among these microbiomes and a potential role of bacteria in sialolith formation. During this study we wanted to check the applicability of saliva as reference sample in the quantitative proteomic analysis of sialoliths. The various comparisons between saliva collected form healthy donors and saliva collected form patients with sialolithiasis showed, that there are no statistically significant differences between these 2 samples. However, the quantification of proteins in salivary stones with any saliva sample brought many potential protein biomarkers, involved in calcification process leading to sialoliths formation. Second part was verification of previously established protein profiles of different types of sialoliths: calcified (CAL), lipid (LIP) and mixed (MIX). That study used pooled sialolith samples as reference and an experiment conducted in the same way now showed different results – only some part of quantified proteins was the same. This fact is reasonable, because the protein composition of reference sample depends on the protein composition of all pooled samples, so because it was set of different samples, the proteome of control sample was also different. Because of these differences, it was impossible to classify the sialoliths based only on the protein profiles established earlier. To make this more feasible, the spectroscopic studies should be repeated and, based on the results, the proteomic profiling of sialoliths should be done again, but with more repeatable reference sample, for example, saliva collected form healthy donors. Despite the lack of differences between the compared saliva samples, we selected set of 33 proteins involved in the formation of salivary stones. They are high confident proteins, which were quantified and described also during previous study, where the different salivary stone types were profiled. Enrichment analysis of these proteins confirmed our previously drawn conclusions, that sialolithiasis is associated with the presence of bacteria, activity of immune system, formation of neutrophils cellular traps and altering the levels of calcium or lipids. METHODOLOGY Collecting samples All salivary stone and saliva samples were collected from patients under the care of the Department of Otolaryngology at the Medical University of Gdańsk. Patients were included in the study only after signing the necessary written consent and approval by the Independent Bioethics Commission at the Medical University of Gdańsk. All methods were performed in accordance with the relevant guidelines and regulations. Process of collecting the salivary stone samples was the same as described earlier [ 29 ], optimized and standardized according to the applicable routine protocol from the Clinic of Otolaryngology with the Department of Oral and Maxillofacial Surgery at The University Clinical Centre in Gdańsk. The saliva samples were collected before surgery by spitting into a sterile falcon tube (20ml of saliva). The patient could not eat and clean their teeth 1 hour before ordering; besides, before the spitting patient was washing his oral cavity with water for 1 minute. The proteolysis inhibitor was added to prevent the salivary proteins from proteolysis (water with trifluoroacetic acid). Secured saliva samples were stored at 80°C for further experiments and then transported to the Intercollegiate Faculty of Biotechnology of the University of Gdańsk and Medical University of Gdańsk on dry ice. Saliva samples from healthy donors were collected from faculty members who did not show signs of sialolithiasis or other salivary glands dysfunctions. Sialoliths of submandibular origin were removed during endoscopic, transoral or transcervical surgery. After that, the salivary stone samples were washed with use buffer (25 mM NH 4 HCO 3 ) and then stored in sterile falcon tubes at 80°C for further experiments. The pictures of most of salivary stone samples are included (Supplementary File 1). Protein extraction from salivary stones The used approaches of processing the sialolith samples were based on protocols described in previous publications [ 28 , 29 ]. To perform the extraction of proteins from sialolith samples, the first step was crushing the salivary stones into the powder. It was served using mortar. To extract as much proteins as possible several lysis buffer, sample amount and sonication condition combinations were tested earlier and the most optimal were chosen. Sonication part was inspired by one of the papers [ 94 ]. Finally, 50mg of powdered sialolith was treated with 250µl of lysis buffer (3% SDS, 100 mM Tris– HCl pH 8,0, 50 mM DTT). First sample portion was treated in standard way: after adding the lysis buffer the samples were incubated at 95°C for 15 min with mixing, centrifugated and then the supernatant was collected. Second sample portion was processed using sonicator (Q700, Sound Enclosure, Cup Horn & Chiller, QSonica) for 15 min (cycle: 15s ON/5s OFF, amplitude: 75%, 20°C). Then, the samples were incubated at 95°C for 15 min with mixing and after that they were again sonicated under the same conditions as earlier. After centrifugation supernatant was collected. The concentration of both supernatants was measured and equally amounts of proteins were combined for each sample. Protein extraction from saliva Similar approach was used to process the saliva samples. Again, the most optimal protocol was chosen after testing several combinations. Finally, 50µl of saliva was treated with 250µl of lysis buffer (1% SDS, 100 mM Tris– HCl pH 8,0, 50 mM DTT). Again, first sample portion was treated in standard way, second sample portion was processed using sonicator, as described above, for 15 min (cycle: 15s ON/5s OFF, amplitude: 50%, 20°C). The concentration of both supernatants was measured and equally amounts of proteins were combined for each sample. Digestion of salivary stone and saliva samples For digestion part, protocol based on standard FASP and sonication-aided FASP was used [ 94 , 95 ]. First portion of extracted proteins was digested according standard FASP approach on a 10 kDa membrane. Digestion lasted overnight. Second portion of extracted proteins was processed with sonication, also on a 10 kDa membrane. Trypsin was added and the samples were sonicated for 15 min (cycle: 15s ON/5s OFF, amplitude: 50%) at 37°C. After that, digestion was done. The concentration of both peptide fractions was measured and equally amounts of peptides were combined for each sample and prepared for MS analysis by final clean-up on C18 (exchange disks 3 M EmporeTM) StageTips according to described protocol [ 96 ]. Construction of the spectral library To maximize the number of quantified proteins the final spectral library intended for SWATH-MS analysis consisted of various spectra: Single DDA spectra for each salivary stone and saliva sample described in this work; Single DDA spectra for each salivary stone sample described and recorded during our previous work [ 29 ] DDA spectra for pooled sialolith protein fractions after separation in gel (SageELF) and digested according to FASP protocol, described and recorded during our previous work [ 29 ] DDA spectra for peptide fractions after FASP digestion and chromatographic separation in alkaline pH; described and recorded during our previous work [ 29 ] Qualitative (DDA) LC-MS/MS analysis This part was performed as described earlier [ 29 ]. LC–MS/MS analysis was performed on Triple-TOF 5600 + mass spectrometer (AB Sciex LLC, Framingham, MA, USA) connected with Ekspert MicroLC 200 Plus System (Eksigent, Dublin, CA, USA). The Analyst TF 1.7.1 software (SCIEX) controlled the whole system. The chromatographic gradient for each MS run was 11–42% B (A: H2O + 0,1% FA; B: 100% ACN + 0,1% FA) in 60 min. ChromXP C18CL column (3µm, 120Å, 150×0.3mm) was used to perform the chromatographic separation. The spectra were registered in information dependent acquisition (IDA) mode to perform qualitative analysis and build the library. Each cycle comprised precursor spectra accumulation in 100ms in the range of 400–1200 m/z followed by top 20 precursor ion spectra accumulation in 50ms in the range of 100–1800 m/z, resulting in a total cycle time of 1.15s. Formerly fragmented precursor ions were dynamically excluded. Quantitative (DIA) LC-MS/MS analysis This part was performed almost the same as described earlier [ 29 ]. The equalized frequency of precursor ions and coverage of the precursor mass range of 400–1200 m/z was used to construct the set of 25 transmission windows of variable width with SWATH® Variable Window Assay Calculator (AB Sciex LLC, Framingham, MA, USA). The collision energy for each window was calculated for + 2 to + 5 charged ions centered upon the window with a spread of five. The SWATH-MS survey scan was acquired in the range covered by constructed windows at the beginning of each cycle with an accumulation time of 50ms. Following SWATH-MS/MS spectra, product ion scans were collected in the range of 100 to 1800m/z in 39,995ms, which resulted in a total cycle time of 1,0999 s. Spectra were registered in 3 technical replications in data-independent acquisition (DIA) mode for each sample [ 97 ]. LC-MS/MS data processing First, the DDA spectra for each clinical sample were analysed in PeaksSTUDIO software with the following settings: instrument: TripleTOF; fragmentation method: CID; acquisition: IDA; Parent Mass Error Tolerance: 15.0 ppm; Fragment Mass Error Tolerance: 0.05 Da; Precursor Mass Search Type: monoisotopic; Digestion: trypsin, Max Missed Cleavages: 2; Digest Mode: Specific; Peptide Length Range: 6–45; Fixed Modifications: Carbamidomethylation (+ 57.02); Variable Modifications: Formylation (+ 27.99) and Oxidation (M) (+ 15.99), Max Variable PTM Per Peptide: 2. The spectra were processed against the entire bacterial database with oral cavity keyword filter (Uniprot, 16.01.2026). In terms of quantitative analysis, data were processed in PeakView 2.2 software (SCIEX), and DIA spectra were processed against the created sialoliths and saliva combined spectral library, which was constructed using ProteinPilot 4.5 software (Sciex; Homo sapiens database, Uniprot, 23.11.2024) and all DDA spectra described in Construction of the spectral library part. After processing all of the clinical samples registered in DIA mode in PeakView software according to settings described by Lewandowska [ 97 ], SWATH data were generated. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral.proteomexchange.org ) via the PRIDE partner repository [ 98 ] with the dataset identifier PXD072871. Statistical and enrichment analysis This part of analysis was performed based on the previous project [ 29 ]. Qualitative data were processed in Excel. SWATH data from PeakView software were exported to MarkerView 1.2.1.1 software (Sciex). Data were normalized using the total area sums (TAS) approach. Then, the output table was exported to Perseus 2.1.1.0 software (MaxQuant) [ 99 ] to perform statistical tests and calculate fold change (FC) values. Enrichment analysis was performed, using STRING 12.0 [ 100 ]. For data visualization BioRender [ 101 ], Cytoscape 3.10.2 [ 102 ], InteractiVenn [ 103 ] and SRplot tool [ 104 ] were used. Declarations Ethics approval and consent to participate: The study protocol was approved by the Regional Bioethics Committee of Gdansk Medical University, Poland, with approval NKBBN/452/2019. Consent for publication: Not applicable Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [98] partner repository with the dataset identifier PXD072871. Competing Interests Statement: The authors declare that they have no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8967608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":606215992,"identity":"1b6ee562-0c92-4bca-a9c6-4b16824d4e13","order_by":0,"name":"Natalia 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sialolith and saliva samples (each technical replicate is shown separately).\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/e1e965b9f925148242247bb5.jpeg"},{"id":104874158,"identity":"567c3966-55e9-4144-a25a-bb57c113cc7a","added_by":"auto","created_at":"2026-03-18 08:29:17","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1810139,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram shows overlapping of bacterial species identified among the compared sample groups.\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/f5d3f3603359a6d48a084cfe.jpeg"},{"id":104874126,"identity":"5504848a-a11c-40ac-aa40-cf6affe63c2b","added_by":"auto","created_at":"2026-03-18 08:29:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1380398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eBox plots show the distribution of log2FC values for the set of common for all tested sialolith samples quantified proteins (control: respective saliva samples collected from the same patient; p-value \u0026lt; 0,05, log2FC ≥ 0,6 for up-regulated proteins, log2FC ≤ -0,6 for down-regulated proteins); \u003cstrong\u003eB)\u003c/strong\u003e Correlation plot presenting the t-test p-values for pairwise comparisons of quantitative data of common proteins among tested salivary stone samples. Color scale corresponds to p-values: red indicates lower values, blue indicates higher values.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/6539f95f9a4d13c8ec1308d0.jpg"},{"id":104874069,"identity":"bf366e7c-15c3-4acb-a30e-b847a69aa895","added_by":"auto","created_at":"2026-03-18 08:28:56","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":681506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eVenn diagram shows overlapping of quantified proteins (identified in more that 50% of sialolith samples) depending on the selected control: 1) pooled salivary stone samples, 2) pooled saliva samples from healthy donors, 3) pooled saliva samples from patients with sialolithiasis. \u003cstrong\u003eB)\u003c/strong\u003e Box plots show the distribution of median log2FC values for the set of overlapped proteins depending on the selected control. T-test p-values for pairwise comparisons and ANOVA p-value are presented on the graph.\u003c/p\u003e","description":"","filename":"Figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/3990a2019bdf2d2aafb756d8.jpeg"},{"id":104874020,"identity":"4631238e-2d8d-46fa-a8f3-07a292f4d1a8","added_by":"auto","created_at":"2026-03-18 08:28:51","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1504182,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagrams presenting the overlapped bacterial genus detected in salivary stones, saliva from patients with sialolithiasis and saliva from healthy donors both at proteomic and transcriptomic levels [56].\u003c/p\u003e","description":"","filename":"Figure5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/bdcf5ec8d2be71ce604d8050.jpeg"},{"id":104874104,"identity":"8980aa9e-7628-46fc-9c5b-f365a8205f6f","added_by":"auto","created_at":"2026-03-18 08:28:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1428896,"visible":true,"origin":"","legend":"\u003cp\u003eThe Cytoscape visualisation of the STRING-generated network is composed of experimentally verified protein–protein interactions among the statistically significant up-regulated and down-regulated proteins quantified in more that 50% of sialolith samples (control: pooled sample of all sialolith samples). The size of nodes corresponds to the frequency of occurrence of protein among all of the samples: bigger nodes represent higher frequency, smaller nodes represent lower frequency. Fill colors correspond with overlapping of proteins identified in different types of salivary stones: orange – CAL, green – LIP, blue – MIX. Border color corresponds to type of protein regulation: red - up-regulation of protein among all the samples, blue - down-regulation of protein, and green - level of regulation of protein is varied among the samples.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/12d74fc3e0a375cc73d2fb26.png"},{"id":104874159,"identity":"fbdbaada-53e6-4524-ae20-d59a0fd92ef9","added_by":"auto","created_at":"2026-03-18 08:29:17","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1203058,"visible":true,"origin":"","legend":"\u003cp\u003eBar plot presenting the results of enrichment analysis for the proteins quantified in more that 50% of sialolith samples (control: pooled sample of all sialolith samples), considering Biological Process (GO), Cellular Component (GO), Molecular Function (GO), KEGG and Reactome terms.\u003c/p\u003e","description":"","filename":"Figure7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/cac02d3e5cea476c6089db05.jpeg"},{"id":104874026,"identity":"fb84b780-c96c-4e94-b06b-498ea6e42dab","added_by":"auto","created_at":"2026-03-18 08:28:53","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2590595,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eThe Cytoscape visualisation of the STRING-generated network is composed of experimentally verified protein–protein interactions among the set of statistically significant common for all tested sialolith samples quantified proteins (control: respective saliva samples collected from the same patient). Fill colors correspond with overlapping of proteins identified in different types of salivary stones: orange – CAL, green – LIP, blue – MIX. Blue border color indicates, that all proteins were down-regulated among all the samples. \u003cstrong\u003eB)\u003c/strong\u003e Bar plot presenting the results of enrichment analysis for this set of proteins, considering Biological Process (GO), Cellular Component (GO), Molecular Function (GO) and Reactome terms.\u003c/p\u003e","description":"","filename":"Figure8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/9d343a0318edc965d2fb6460.jpeg"},{"id":104873992,"identity":"73f23ae6-081f-47ec-b9f6-a1c3b714bf7f","added_by":"auto","created_at":"2026-03-18 08:28:39","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1147391,"visible":true,"origin":"","legend":"\u003cp\u003eThe Cytoscape visualisation of the STRING-generated network is composed of experimentally verified protein–protein interactions among the overlapped statistically significant up-regulated and down-regulated proteins (identified in more that 50% of sialolith samples) between sets of quantified proteins, depending on the selected control: 1) pooled salivary stone samples, 2) pooled saliva samples from healthy donors, 3) pooled saliva samples from patients with sialolithiasis. Fill colors correspond with overlapping of proteins identified in different types of salivary stones: orange – CAL, green – LIP, blue – MIX. Border color corresponds to type of protein regulation: red - up-regulation of protein among all the samples, blue - down-regulation of protein, and green - level of regulation of protein is varied among the samples.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/0fc7f747686be91bc0d2e873.png"},{"id":104873914,"identity":"408acdc6-5ba0-4af8-8c1c-7dbe2e84ecaf","added_by":"auto","created_at":"2026-03-18 08:28:32","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1097224,"visible":true,"origin":"","legend":"\u003cp\u003eBar plot presenting the results of enrichment analysis for the overlapped proteins (identified in more that 50% of sialolith samples) between sets of quantified proteins, depending on the selected control: 1) pooled salivary stone samples, 2) pooled saliva samples from healthy donors, 3) pooled saliva samples from patients with sialolithiasis, considering Biological Process (GO), Cellular Component (GO), Molecular Function (GO), KEGG and Reactome terms.\u003c/p\u003e","description":"","filename":"Figure10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/5c6ce45bdca7ed87c6152666.jpeg"},{"id":104874024,"identity":"5036ee31-9913-40a6-8a3c-d1fe914dbc1f","added_by":"auto","created_at":"2026-03-18 08:28:53","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":3166227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eThe Cytoscape visualisation of the STRING-generated network is composed of experimentally verified protein–protein interactions among the overlapped between at least 2 compared sets statistically significant up-regulated and down-regulated proteins between 4 sets of proteins quantified during this analysis: proteins identified in more that 50% of sialolith samples with 1) pooled sialolith samples, 2) pooled saliva from healthy donors, 3) pooled saliva from patients with sialolithiasis as reference samples and 4) common for all tested sialolith samples proteins with respective saliva samples collected from the same patient as reference samples. Fill colors correspond with overlapping of proteins identified in different types of salivary stones: orange – CAL, green – LIP, blue – MIX. Border color corresponds to type of protein regulation: red - up-regulation of protein among all the samples, blue - down-regulation of protein, and green - level of regulation of protein is varied among the samples. \u003cstrong\u003eB)\u003c/strong\u003e Bar plot presenting the results of enrichment analysis for this set of proteins, considering Biological Process (GO), Cellular Component (GO), Molecular Function (GO), KEGG and Reactome terms.\u003c/p\u003e","description":"","filename":"Figure11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/321226d8d0c90daf3d377c64.jpeg"},{"id":104874270,"identity":"897b6f3f-6645-484a-b072-151708bf1156","added_by":"auto","created_at":"2026-03-18 08:29:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17566437,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/36bc9e22-7c02-4e91-8fde-320009a7a194.pdf"},{"id":104874113,"identity":"4db4da2a-2330-4bfa-80f1-5404c6e009cb","added_by":"auto","created_at":"2026-03-18 08:29:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1034431,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/0da94ab6f537343be71ed05b.docx"},{"id":104874023,"identity":"220294f9-b081-4d36-877e-1321354b6fd1","added_by":"auto","created_at":"2026-03-18 08:28:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":113179,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/f04df49a8854073d02c36182.xlsx"},{"id":104874172,"identity":"416df55d-bab4-4536-ad85-3dda5c305981","added_by":"auto","created_at":"2026-03-18 08:29:23","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":46601,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/e98057a8190240421d7a1580.xlsx"},{"id":104874127,"identity":"011a6f6b-9c6c-4c3a-aefa-e27ae21f7d03","added_by":"auto","created_at":"2026-03-18 08:29:09","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":110392,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/1154d58dc790d796895c5ec1.xlsx"},{"id":104873885,"identity":"a3f0137d-5ffc-473f-b087-cf74af7d6bd1","added_by":"auto","created_at":"2026-03-18 08:28:09","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":110223,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8967608/v1/b5e97ba6332c731a02a25081.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative proteomic analysis of saliva and salivary stones in sialolithiasis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSialolithiasis is a relatively uncommon and underrecognized disorder characterized by the formation of calcified deposits, known as sialoliths or salivary stones, which occur in approximately 1\u0026ndash;2% of the population. These stones develop within the salivary ducts or glands, most frequently in the submandibular glands, and less often in the parotid or sublingual glands. Clinical complications arise when a sialolith enlarges sufficiently to obstruct salivary flow, leading to pain\u0026mdash;particularly during meals\u0026mdash;swelling of the affected gland, and occasionally fever. The condition is often accompanied by bacterial infection and purulent inflammation of glandular tissues. At present, surgical removal remains the only effective treatment option [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the spectroscopic studies there was proposed classification of salivary stones. They were divided into three types: calcified (CAL), lipid (LIP), and mixed (MIX). According to results of spectroscopic analysis, these established sialolith types differ from each other in their developmental pathways, when the balance between calcium and lipid components becomes disrupted [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mechanism of biocalcification underlying sialolith formation remains poorly understood. Sialoliths exhibit complex structural organization, typically comprising an inner core surrounded by concentric layers. Depending on the stone type, these layers may be primarily inorganic\u0026mdash;with a well-characterized structure\u0026mdash;or organic, containing incompletely defined constituents such as proteins, lipids, and bacteria. Both commensal and pathogenic microorganisms have been identified within these layers, supporting the hypothesis that bacteria may play a role in biocalcification and stone development [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Conversely, other studies have shown that epithelial cells of the salivary glands secrete peptides and proteins involved in digestion, lubrication, mineralization, tissue protection, buffering, and antimicrobial defense, so any changes in their levels may be crucial for development of sialolithiasis [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. One study found that neutrophil extracellular trap (NET) formation plays a crucial role in sialolith development. NETs occur when neutrophils release chromatin and granular proteins found in saliva in the presence of bacteria. NETs often aggregate into structures called aggNETs, which act as \"glue,\" binding proteins and calcium crystals to form macroscopic sialoliths. NETs have been identified as contributors to deposit formation in other organs as well. Targeting NET formation could be a promising strategy to prevent salivary stone development [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur working hypothesis posits that concentration of salivary or infection-derived proteins influences the calcium\u0026ndash;lipid equilibrium, promoting sialolith formation. Identifying potential biomarkers could therefore provide insight into the biocalcification mechanisms driving this condition. Findings from our previous studies have highlighted several promising biomarker candidates, consistent with the current classification of salivary stones. Enrichment analysis confirmed, that these proteins are mainly associated to immune response, mainly because of the presence of pathogens, such as bacteria. They were also responsible for regulation of calcium or lipids balance and maintaining the structure of extracellular regions structure. To validate these potential markers, a comparative proteomic analysis of sialoliths and saliva from affected patients versus saliva from healthy individuals is required. Despite saliva\u0026rsquo;s inherent instability, such comparisons could yield clinically relevant information, revealing proteins specifically associated with sialolithiasis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is possibility to detect many biomarkers in saliva in the terms of oral diseases (lichen planus, periodontitis, and primary Sj\u0026ouml;gren\u0026rsquo;s syndrome): interleukins (IL1, IL4, IL6, IL8, IL10), C-reactive protein (CRP), matrix metalloproteinases (MMP8, MMP9), pro-epidermal growth factor (EGF) and parotid secretory protein (PSP) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Saliva is also a valuable material for detecting biomarkers associated with various inflammatory and systemic diseases, including pneumonia (CRP), bowel disease (interleukins), diabetes (MMPs, melatonin, lysozyme, glucose), and Cushing\u0026rsquo;s disease (cortisol) [\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It can also be used to diagnose infectious diseases\u0026mdash;for example, by detecting antibodies against human immunodeficiency virus (HIV) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMass spectrometry (MS) coupled with liquid chromatography (LC) enables the high-throughput identification and quantification of proteins within a single analytical run. However, achieving reliable results depends on optimized protein extraction, digestion, and MS acquisition protocols. Given the limited availability of clinical material, optimization is essential for reproducible proteomic outcomes. In our last study (Musiał et al. \u003cem\u003eOptimization of protein extraction and digestion workflows for proteomic analysis of saliva, salivary stones and kidney stones\u003c/em\u003e, Frontiers in Molecular Biosciences, 2026, accepted for publication) we checked the validity of optimized protocols and we confirmed, that it is possible to detect some repeated proteins in saliva and salivary stones. Despite the small sample size, our findings point the potential of optimized proteomics in enhancing biomarker discovery and the understanding of pathological calcification in clinical background.\u003c/p\u003e \u003cp\u003eThe aim of this study was validation of saliva as reference sample during protein profiling of sialoliths. In the first place the differences between saliva from healthy donors and saliva from patients with sialolithiasis should be identified, including the presence of bacterial proteins. Thanks to that the establishment of sialoliths proteome would be more reliable, because of more repeatable protein composition of saliva than proteome of pooled sialoliths sample. Important part is also validation of previously established protein profiles of different types of salivary stones.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe proteomic analysis was performed on a set of clinical samples including 17 sialoliths and 12 saliva samples in total. Specific data are presented in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The pictures of processed salivary stone samples are presented in the \u003cb\u003eSupplementary File 1\u003c/b\u003e. All of the clinical samples were processed according to the FASP methodology, but sonication enhancement was also applied \u0026ndash; after the digestion performed based on 2 different protocols (standard FASP and sonication-aided FASP) equally amounts of obtained peptides were combined for each sample and prepared for MS analysis. During this analysis both DDA (1 replicate) and DIA (3 replicates) spectra were registered for each sample.\u003c/p\u003e \u003cp\u003eTo create spectral library, the spectra of 4 groups of samples were used: single DDA spectra for each salivary stone and saliva sample described in this work; single DDA spectra for each salivary stone sample described and recorded during our previous work [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; DDA spectra for pooled sialolith protein fractions after separation in gel (SageELF) and digested according to FASP protocol, described and recorded during our previous work [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; DDA spectra for peptide fractions after FASP digestion and chromatographic separation in alkaline pH, described and recorded during our previous work [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thanks to that we were able to build the spectral library containing 1428 proteins. After the manual verification of the spectral library we were able to quantify 141 proteins. Data processing and statistical analysis allowed us to identify up-regulated and down-regulated proteins, relatively to chosen control group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSialolith and saliva samples summary.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003epatients with sialolithiasis\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# sialolith samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# saliva samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e- 6 with saliva samples (1\u0026ndash;6)\u003c/p\u003e \u003cp\u003e- 11 without saliva samples (A-K)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1\u0026ndash;6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ehealthy donors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (A-F)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrincipal component analysis shows the grouping of the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PCA clearly shows, that the samples of saliva from healthy donors and saliva from patients with sialolithiasis are grouped together, suggesting the similarity of proteins composition, while proteomes of salivary stones are more variable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDetection of bacterial proteins in sialoliths, salivary from patients with sialolithiasis and saliva from healthy donors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst, we examined the DDA spectra of all clinical samples to determine whether bacterial proteins could be detected. Raw data were processed against a bacterial protein database using an oral cavity keyword filter. Proteins from multiple bacterial species were identified: 29 species in sialolith samples, 7 species in saliva from patients with sialolithiasis, and 21 species in saliva from healthy donors. The identified bacterial species are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The overlap of bacterial species among the compared sample groups is illustrated using a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of identified bacterial species identified among the compared sample groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003esalivary stones\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esaliva from patients with sialolithiasis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esaliva from healthy donors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActinomyces bowdenii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eArachnia propionica\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBacteroides heparinolyticus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBifidobacterium dentium\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCapnocytophaga catalasegens\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCapnocytophaga felis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eConchiformibius kuhniae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eConchiformibius steedae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eDentiradicibacter hellwigii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFusobacterium canifelinum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFusobacterium nucleatum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFusobacterium pseudoperiodonticum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLactobacillus crispatus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eParvimonas micra\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eRodentibacter pneumotropicus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSchaalia canis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus dentalis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus gingivalis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus halitosis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus lingualis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus minor\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus salivarius\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus sanguinis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus troglodytae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eTannerella forsythia\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eUruburuella suis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eUruburuella testudinis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVeillonella nakazawae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVitreoscilla massiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eActinomyces bowdenii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eConchiformibius steedae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFusobacterium canifelinum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eParvimonas micra\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus minor\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus salivarius\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus troglodytae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eActinomyces bowdenii\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBacteroides heparinolyticus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBifidobacterium dentium\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCapnocytophaga felis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eConchiformibius kuhniae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eConchiformibius steedae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFusobacterium canifelinum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eParvimonas micra\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSchaalia canis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus dentalis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus gingivalis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus halitosis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus lingualis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus minor\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus salivarius\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus sanguinis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus troglodytae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eTannerella forsythia\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVeillonella nakazawae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVitreoscilla massiliensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVitreoscilla stercoraria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eQuantitative analysis of sialoliths protein composition, taking into account the defined protein profile of different types of salivary stones\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the terms of quantitative analysis, in the first place we performed exactly the same analysis as in our previous paper [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] \u0026ndash; based on the DIA spectra and constructed spectral library the relative quantitative proteomic analysis was done. We were able to identify statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0,05) up-regulated (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026ge;\u0026thinsp;0,6) and down-regulated (log\u003csub\u003e2\u003c/sub\u003eFC \u0026le; -0,6) proteins in sialoliths (ratio between each sialolith sample and pooled sample of all sialoliths samples). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows detailed information how many proteins were quantified for each tested sample. Besides, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (Supplementary File 2)\u003c/b\u003e present comprehensive results of quantitative analysis. Based on these results we were trying to identify each sialolith sample as a representative sample of one of the three known types of salivary stones: CAL, LIP or MIX. To achieve that, we compared the sets of proteins established during our previous research [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] as standard protein profile of each type and the sets of proteins detected during this research. The number of overlapped between these two sets proteins is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. What is more, we also checked the unique proteins for each sialolith type.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAll tested salivary stone samples summary detailed data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eQuantify proteomic analysis (control: salivary stones (pooled); p-value\u0026thinsp;\u0026lt;\u0026thinsp;0,05)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e# proteins overlapped with proteins defined for different types of sialoliths (# unique proteins for that type of sialoliths)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eANOVA p-value for comparisons of control samples for group of overlapped proteins quantified by calculating the ratio between sialolith sample and control sample:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003et-test p-value for pairwise comparisons of control samples for group of overlapped proteins quantified by calculating the ratio between sialolith sample and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003econtrol sample\u003c/span\u003e:\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# up-regulated proteins (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026ge;\u0026thinsp;0,6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# down-regulated proteins (log\u003csub\u003e2\u003c/sub\u003eFC \u0026le; -0,6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMIX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003esalivary stones (pooled) vs saliva from healthy donors (pooled)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003esalivary stones (pooled) vs saliva from patients with sialolithiasis (pooled)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003esaliva from healthy donors (pooled) vs saliva from patients with sialolithiasis (pooled)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#E\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#F\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#H\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#J\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e#K\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDifferences between sialoliths and saliva collected from different patients\u003c/h2\u003e \u003cp\u003eNext step was proteomic analysis of saliva and sialolith samples collected from each patients. We had six of these sets of paired samples. Again, we performed relative quantitative proteomic analysis to detect statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0,05) up-regulated (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026ge;\u0026thinsp;0,6) and down-regulated (log\u003csub\u003e2\u003c/sub\u003eFC \u0026le; -0,6) proteins in sialoliths, but in this case the referent samples were saliva samples from respective patients. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the number of quantified protein in each sialolith sample. The detailed data are shown in the \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (Supplementary File 3)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA table with the number of up-regulated and down-regulated proteins for each sialolith sample (control: respective saliva samples collected from the same patient), which are statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0,05) and the log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026ge;\u0026thinsp;0,6 for up-regulated proteins and log\u003csub\u003e2\u003c/sub\u003eFC \u0026le; -0,6 for down regulated proteins.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003esample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e#2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e#3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e#4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e#5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e#6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e# up-regulated proteins (log\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eFC\u0026thinsp;\u0026ge;\u0026thinsp;0,6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e# down-regulated proteins (log\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eFC \u0026le; -0,6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo check, if the protein profile is similar for each salivary stone sample, we took the proteins common for all samples (23 proteins) and presented the distribution of their log\u003csub\u003e2\u003c/sub\u003eFC values as the box plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). ANOVA p-value was calculated (p-value\u0026thinsp;=\u0026thinsp;0,003872). What is more, the t-test p-values for pairwise comparisons of quantitative data of common proteins among tested salivary stone samples were also calculated and they are presented as correlation plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferences between saliva from patients with sialolithiasis and saliva from healthy donors\u003c/h3\u003e\n\u003cp\u003eIn the first place we wanted to check if there are some proteins, which level is statistically different between saliva from healthy donors and saliva from patients with sialolithiasis. To verify that with high confidence we performed 3 different comparisons: 1) saliva from patients with sialolithiasis (median value of all samples) vs saliva from healthy donors (median value of all samples); 2) saliva from patients with sialolithiasis (pooled) vs saliva from healthy donors (pooled); 3) each saliva from patients with sialolithiasis vs saliva from healthy donors (pooled). For each comparison the t-test was performed, but, based on p-values, there are very few statistically different proteins between these two types of saliva samples (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0,05; up-regulated proteins: log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026ge;\u0026thinsp;0,6; down-regulated proteins: log\u003csub\u003e2\u003c/sub\u003eFC \u0026le; -0,6). Comparing median values of all samples between saliva from patients with sialolithiasis and saliva from healthy donor (comparison 1.), there is only 1 statistically significant protein: Salivary acidic proline-rich phosphoprotein \u0026frac12; (PRH1; log\u003csub\u003e2\u003c/sub\u003eFC = -1,00). For comparison 2., where pooled saliva from patients with sialolithiasis was compared with pooled saliva from healthy donors, there are 2 statistically different proteins: Hemoglobin subunit beta (HBB; log\u003csub\u003e2\u003c/sub\u003eFC = -1,34) and Junction plakoglobin (JUP; log\u003csub\u003e2\u003c/sub\u003eFC = -6,22). In the case of separately compared saliva samples, where the control was pooled saliva from healthy donors (comparison 3.), only for saliva sample #4 statistically significant proteins were detected: Hemoglobin subunit beta (HBB; log\u003csub\u003e2\u003c/sub\u003eFC = -1,84) and Submaxillary gland androgen-regulated protein 3B (SMR3B; log\u003csub\u003e2\u003c/sub\u003eFC = -3,17).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eComparison of protein profile of sialoliths depending on the selected control sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLast part was comparison the proteomes of salivary stones established depending on the selected reference sample. First, we performed the same quantitative proteomic analysis as previously, where control sample was pooled sample of all sialoliths samples, but here we chose the saliva collected from healthy donors (pooled) \u0026nbsp;and saliva collected from patients with sialolithiasis (pooled) as reference samples. The statistically significant (p-value \u0026lt; 0,05) up-regulated (log2FC ≥ 0,6) and down-regulated (log2FC ≤ -0,6) proteins were identified. The detailed data are presented in the \u003cstrong\u003eTable S1 (Supplementary File 4)\u003c/strong\u003e and \u003cstrong\u003eTable S1 (Supplementary File 5)\u003c/strong\u003e, respectively. Then we selected proteins, which were quantified in more that 50% of sialolith samples for each comparison – there were 92 proteins for analysis with pooled salivary stone samples as reference, 102 proteins for analysis with pooled saliva samples from healthy donors as reference and 96 proteins for analysis with pooled saliva samples from patients with sialolithiasis as reference. The overlapping of these set of proteins is presented on the Venn diagram (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). There are 76 common proteins. Next, the distribution of median log\u003csub\u003e2\u003c/sub\u003eFC values of these proteins is shown as box plots (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). ANOVA and t-test were conducted to calculate p-values an to check if there are statistically significant differences between the groups. The p-values for the overlapped sets of proteins are shown on the \u003cstrong\u003eFigure 4B\u003c/strong\u003e, but the ANOVA and t-tests were also conducted for each separate sialolith sample and the p-values for these comparisons are shown in the \u003cstrong\u003eTable 3\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eDescribing sialolithiasis, there is still no confirmed cause or factor leading to biocalcification process and formation of stones in salivary glands or salivary ducts. During our previous studies we were trying to identify potential protein biomarkers, which are crucial in this pathological state. To perform this analysis we were comparing the sialolith samples between each other, but we wanted to find proper control samples group. We decided to use saliva as control sample \u0026ndash; during our previous work (Musiał et al. Optimization of protein extraction and digestion workflows for proteomic analysis of saliva, salivary stones and kidney stones, Frontiers in Molecular Biosciences, 2026, accepted for publication) we checked the applicability of this comparison and we found some set of common proteins. However, it was qualitative analysis based on DDA spectra, that is why we wanted to performed quantitative analysis based on DIA spectra. In this way it is possible to check the relative level of proteins, comparing salivary stone samples and saliva samples. The disturbances of proteins level may indicate the connection between their functions and biocalcification mechanism leading to the sialoliths formation.\u003c/p\u003e \u003cp\u003eIn the first place we wanted to check if it is possible to identify some bacterial proteins in our clinical samples. Processing the raw data against protein database including bacteria species associated with oral cavity we were able to detect bacterial proteins present in sialoliths, saliva from patients with sialolithiasis and saliva from healthy donors. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the bacterial species for which proteins were identified in the analyzed samples. The highest number of species was detected in sialoliths (29 species), followed by saliva from healthy donors (21 species), while the lowest number was observed in saliva from patients with sialolithiasis (7 species). Importantly, all species detected in saliva from patients with sialolithiasis were also present in the other sample types. All identified bacterial species are considered typical members of the oral cavity microbiota [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Many of the identified bacteria belong to opportunistic pathogens, particularly species of \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eFusobacterium\u003c/em\u003e [\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], whereas \u003cem\u003eTannerella forsythia\u003c/em\u003e is considered a true pathogen [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe compared the bacterial species detected in salivary stones in the present study with those identified in our previous research [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Four identical species were detected in both studies: \u003cem\u003eFusobacterium canifelinum\u003c/em\u003e, \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e, \u003cem\u003eFusobacterium pseudoperiodonticum\u003c/em\u003e, and \u003cem\u003eTannerella forsythia\u003c/em\u003e. At the genus level, representatives of the same four genera - \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eTannerella\u003c/em\u003e - were identified. These genera comprise bacteria that are typical inhabitants of the oral cavity, predominantly acting as opportunistic pathogens.\u003c/p\u003e \u003cp\u003eA previous study analyzed the microbiome of sialoliths, as well as saliva from patients with sialolithiasis and from healthy donors, at the transcriptomic level [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The authors aimed to identify the bacterial genera present in the analyzed samples. We compared their findings with the data obtained in the present study, and the results are illustrated using Venn diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sets of bacteria detected by proteomic analysis were noticeably less diverse, most likely due to the smaller number of analyzed samples (proteomic analysis: 6 affected patients; transcriptomic analysis: 27 affected patients). Nevertheless, several bacterial genus were common to both datasets. The most frequently overlapping genera included \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eCapnocytophaga\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eTannerella\u003c/em\u003e. Comparative analysis of proteomic and transcriptomic data indicates that the salivary microbiome of healthy donors and patients with sialolithiasis is highly similar. In contrast, the microbiome of sialoliths shows differences, although some bacterial genus are shared. This overlap suggests interactions between the salivary stone environment and saliva, which may contribute to pathological calcification and, consequently, to deposit formation in the salivary glands or ducts.\u003c/p\u003e \u003cp\u003eFirst part of quantitative part of this research was conducting the proteomic analysis of the sialoliths compound in the exactly the same way as during our previous research, where our control was pooled sample of all tested salivary stone samples. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the exact numbers of detected in each sample up-regulated and down-regulated statistically significant proteins. However, we could not expect exactly the same results, because of the protein composition of pooled sample \u0026ndash; there was set of different sialolith samples, so the pooled control sample was also different. We wanted to check if we were able to detect some repeating proteins, especially taking into account the division of the sialoliths into 3 different groups (CAL, LIP an MIX). During previous research the set of proteins was in the first place analyzed using spectroscopic methods and based on the obtained results the stone samples were divided into 3 groups according to the proposed classification [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. We compared the proteins quantified during this study and sets of proteins quantified and established for each sialolith type during previous study. We were analysing 3 sets of proteins: CAL \u0026ndash; 55 proteins, LIP \u0026ndash; 22 proteins, and MIX \u0026ndash; 26 proteins. We were able to detect in all of the sialolith samples some part of proteins, which were earlier established for each type of stone. What is more, in the case of CAL and LIP stones the unique for sialolith type proteins were identified in each sample. About MIX salivary stones, there was only 1 unique protein - Fibrinogen alpha chain (FGA), associated with activity of neutrophils and formation of NETs [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This protein was found in 8 out of 17 samples.\u003c/p\u003e \u003cp\u003eTo discuss the overall protein profile of sialoliths we selected the set of 92 proteins, which were identified in at least 50% of tested samples. Based on that we generated the network of interactions between these proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Border color of the nodes indicates the type of regulation of these proteins. Most of them (78 proteins) were down-regulated among all the samples. Only 1 protein \u0026ndash; Eosinophil cationic protein (RNASE3) \u0026ndash; was up-regulated among all the samples. This protein is ribonuclease with ability to cytotoxicity and binding of heparin, used during inflammation state, also in saliva [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. There were also 13 proteins, which level of regulation was varied among the samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysing the sialoliths classification, we identified 26 proteins defined as proteome of CAL salivary stones, 13 proteins \u0026ndash; LIP stones, and 14 proteins \u0026ndash; MIX sialoliths. The most important are proteins detected in each type of salivary stones and we were able to found 7 of these proteins: Eosinophil cationic protein (RNASE3), Hemoglobin subunit beta (HBB), Neutrophil elastase (ELANE), Cystatin-S (CST4), Cystatin-SN (CST1), Statherin (STATH) and Immunoglobulin gamma-1 heavy chain (P0DOX5). Neutrophil elastase (ELANE) is secreted during inflammation by neutrophils and with the ability to bind the DNA this proteins is associated with formation of neutrophil cellular traps [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Cystatin-S (CST4) and Cystatin-SN (CST1) are responsible for binding the calcium also in the saliva. It may influence the changes in the balance of calcium, leading to the biocalcification process and formation of sialoliths [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Next protein is Statherin (STATH), small protein, which theoretically should be washed-out during digestion procedure performed on the filters with membrane. It is another example of protein influencing the saliva composition, because by inhibition of calcium phosphate salts precipitation, the role of this protein is stabilization of saliva [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Presence of Immunoglobulin gamma-1 heavy chain (P0DOX5) confirms the role of immune system in the formation of salivary stones.\u003c/p\u003e \u003cp\u003eIt was possible to identify repeating proteins, but it is impossible to divided tested salivary stone samples into one of three established groups based on the protein profile. It depends on the chosen control sample \u0026ndash; during previous and present studies the pooled samples, made by mixing all tested samples, was selected as reference, but the set of clinical samples was different, so the protein composition of reference sample was different and, as a consequence, the results of proteomic analysis were varied. To establish constant protein profile of each sialolith type we need another control sample with relative stable and repeatable protein composition. That is why we decided to validate saliva as reference, both collected from patients with sialolithiasis and, more importantly, from healthy donors.\u003c/p\u003e \u003cp\u003eDespite a different set of proteins that have been identified during this study, performed enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) shows, that the roles and functions of quantified proteins are similar to data obtained earlier [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. According to Biological Process Gene Ontology database most of the proteins are associated with immune system processes and defense response to presence of bacteria. It may confirm, that the bacteria can cause formation of sialoliths. What is more, quantified proteins are also responsible for causing the changes in the lipids homeostasis. There are enrichment terms detected, which may indicate this: \u003cem\u003eRegulation of peptidase activity\u003c/em\u003e (17 proteins), \u003cem\u003eNegative regulation of endopeptidase activity\u003c/em\u003e (16 proteins), \u003cem\u003eResponse to lipid\u003c/em\u003e (13 proteins) and \u003cem\u003eCellular response to lipid\u003c/em\u003e (9 proteins) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Besides, 7 proteins are connected to \u003cem\u003eOssification\u003c/em\u003e term. This process has been previously associated to sialoliths formation, because they are formed during calcification process, which is precursor to ossification (bone formation) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In the case of Molecular Function Gene Ontology database, \u003cem\u003eEndopeptidase inhibitor activity\u003c/em\u003e (14 proteins) term was detected, pointing on the process of altering the level of lipids. On the other hand, the balance of calcium level is also disturbed \u0026ndash; the \u003cem\u003eHeparin binding\u003c/em\u003e (7 proteins) and \u003cem\u003eCalcium-dependent protein binding\u003c/em\u003e (5 proteins) terms were found [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Discussing detected Cellular Component Gene Ontology terms, most of them are associated to extracellular region and it may show the role of neutrophil extracellular traps in the biocalcification process. There was also \u003cem\u003eCornified envelope\u003c/em\u003e (4 proteins) term detected, referring to tough and insoluble protein structure, which may be formed in oral mucosa. Lipids play the key role during this process [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. KEGG and Reactome databases also show, that the immune response and extracellular components are crucial during sialoliths formation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext part of this study was checking the pattern of relative level of proteins in sialolith compared to saliva, collected from the same patients with sialoliths. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the number of up-regulated and down-regulated statistically significant proteins, detected in each salivary stone sample. Most of the proteins are down-regulated. We selected the repeating in each sample proteins \u0026ndash; there were 23 of these proteins. Based on the results presented as box plots and statistical tests (ANOVA and t-test), there is no common pattern of distribution of log\u003csub\u003e2\u003c/sub\u003eFC values \u0026ndash; ANOVA p-value\u0026thinsp;=\u0026thinsp;0,003872, so it means that there is a statistically significant difference between the means of at least two groups. T-test p-values indicate, that there are statistically significant differences (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0,05) in distribution of log\u003csub\u003e2\u003c/sub\u003eFC values between sialoliths #1 and #6, #2 and #5, #2 and #6, #3 and #5, #3 and #6. However, all of the overlapped proteins are down-regulated in all tested samples. We prepared the network of the protein-protein interactions for the common set (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). There are 2 proteins, which were described earlier and they were previously identified as a part of proteome of all 3 types of sialoliths (CAL, LIP and MIX): Neutrophil elastase (ELANE) and Cystatin-S (CST4). Neutrophil gelatinase-associated lipocalin (LCN2) and Albumin (ALB) were present both in CAL and MIX sialoliths. LCN2 plays the key role in the transport of hydrophobic molecules, so any changes in the level of this protein may cause the lipids imbalance. Neutrophil gelatinase-associated lipocalin was also identified as a part of NETs [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. On the other hand, the changes in the Albumin (ALB) level may also be reason of biocalcification process because the ability to binding of calcium ions [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Haemoglobin subunit alpha (HBA2) was found both in LIP and MIX salivary stones. There were also 4 proteins identified as a part of protein profile of only CAL stones: Protein S100-A12 (S100A12), Keratin, type II cytoskeletal 2 epidermal (KRT2), Vitronectin (VTN) and Immunoglobulin J chain (JCHAIN). S100A12 regulates immune response and in the presence of bacteria is key member of NETs. This proteins binds also calcium and, forming calprotectin complex, shows antimicrobial properties. Protein S100-A12 and Vitronectin (VTN) were also classified as markers of periodontitis [\u003cspan additionalcitationids=\"CR63 CR64 CR65 CR66 CR67\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The presence of Immunoglobulin J chain (JCHAIN) indicates the activation of immune system, for example because of the presence of bacteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) of this set of proteins and Biological Process Gene Ontology database shows, that most of the proteins are responsible for activation and regulation of immune system, most probably in the presence of bacteria, what may be a proof of the influence of microbes on the calcification process leading to the formation of the sialoliths. The detection of \u003cem\u003eHeparin binding\u003c/em\u003e (4 proteins) Molecular Function Gene Ontology term points on the disturbances of calcium homeostasis. The role of neutrophil extracellular traps in the sialolithiasis may be confirmed by quantification of proteins associated with extracellular components. The analysis of Reactome database complements the above conclusions.\u003c/p\u003e \u003cp\u003eNext, we wanted to check the differences in the protein profile of saliva collected from healthy donors and patients with sialolithiasis. The aim was to verify the applicability of saliva, especially the saliva collected from healthy person, as proper control to perform quantitative proteomic analysis of sialoliths. First look at the Principal Component Analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) may suggest that there aren\u0026rsquo;t many differences between these two groups of samples \u0026ndash; all tested saliva samples are grouped together. To conduct detailed analysis, we performed 3 different comparisons: saliva from patients with sialolithiasis (median value of all samples) vs saliva from healthy donors (median value of all samples), saliva from patients with sialolithiasis (pooled) vs saliva from healthy donors (pooled) and each saliva from patients with sialolithiasis vs saliva from healthy donors (pooled). Unfortunately, according to statistical analysis, there are almost no statistically different proteins between these 2 types of saliva. There are only 4 statistically significant proteins: Salivary acidic proline-rich phosphoprotein \u0026frac12; (PRH1), Hemoglobin subunit beta (HBB; quantified in 2 analysis), Junction plakoglobin (JUP) and Submaxillary gland androgen-regulated protein 3B (SMR3B). Salivary acidic proline-rich phosphoprotein \u0026frac12; (PRH1) is responsible for protection and repairing of dental enamel. This protein inhibits growth of crystals, regulates balance of calcium phosphate and has ability to binding to the bacterial cells [\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] and this may refer to the hypothesis about role of imbalance of calcium and presence of bacteria in biocalcification process. Junction plakoglobin (JUP) plays the key role in cell-cell adhesion as a part of adherens junctions and desmosomes, it is also responsible for cell signaling [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Down-regulation of JUP in saliva was confirmed in the case of autoimmune liver diseases [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Disturbed regulation of Submaxillary gland androgen-regulated protein 3B (SMR3B) may influence the level of lipids by inhibition of endopeptidase activity [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinal step was comparison of protein profiles of salivary stones depending on the selected reference sample. When comparing, we took into account 3 control samples: salivary stones (pooled), saliva from healthy donors (pooled) and saliva from patients with sialolithiasis (pooled). The distribution of median log\u003csub\u003e2\u003c/sub\u003eFC of 76 common proteins, presented as box plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), suggests, that the proteomes of sialoliths are similar, regardless of the selected reference sample. To confirm that, the statistical test were performed: ANOVA, to determine if there are any statistically significant differences between the means of three compared groups, and t-test, to determine if there is a statistically significant difference between the means of two groups. ANOVA p-value\u0026thinsp;=\u0026thinsp;0,9741 indicates that there is no significant difference between the means of the groups being compared. Additionally, t-test p-values for the for pairwise comparisons (0,826; 0,931; 0,886) also indicate that there are no significant differences between the means of 2 compared groups. The ANOVA and t-test were also performed for each separate sialolith sample (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and the fact that each p-value is higher than 0,05 indicates that there are no significant differences between compared reference samples.\u003c/p\u003e \u003cp\u003eWe prepared the network of interactions between the proteins from analysed set (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). There was 1 up-regulated among all of the samples (regardless of the selected reference sample) protein - Eosinophil cationic protein (RNASE3). 61 proteins were down-regulated and 14 had varied type of regulation among the samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTaking into account the established earlier classification of sialoliths and their protein profiles, we quantified 7 proteins present in all stone types (CAL, LIP and MIX). These are the proteins: Eosinophil cationic protein (RNASE3), Cystatin-S (CST4), Cystatin-SN (CST1), Statherin (STATH), Neutrophil elastase (ELANE), Immunoglobulin gamma-1 heavy chain (P0DOX5) and Hemoglobin subunit beta (HBB). All of the proteins were described above.\u003c/p\u003e \u003cp\u003eThe results of the functional analysis of this set of proteins give analogous conclusions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Taking into account the Gene Ontology (Biological Process, Molecular Function and Cellular Component), KEGG and Reactome databases, we can assume, that the quantified proteins are mainly associated with activation of immune system, defence mechanisms in the presence of bacteria, altering the levels of calcium and lipids and regulation of extracellular region structure. Any changes in the homeostasis of these processes may be the reason of the biocalcification process and formation of sialoliths.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo select the potential biomarkers of sialolithiasis, which are involved in pathological biocalcification leading to formation of deposits in salivary glands or salivary ducts, we selected the most frequent proteins among the quantified proteins during the above analysis and established as members of the proteomes of different sialoliths types [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We compared 4 sets of proteins quantified during this analysis: proteins identified in more that 50% of sialolith samples with 1) pooled sialolith samples, 2) pooled saliva from healthy donors, 3) pooled saliva from patients with sialolithiasis as reference samples and 4) common for all tested sialolith samples proteins with respective saliva samples collected from the same patient as reference samples. Proteins, which were overlapped between at least 2 compared sets were then compared to proteins identified during our previous research. In this way we got 33 proteins and the network of their interactions was prepared (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong considered set, there were 8 proteins identified previously in CAL, LIP and MIX sialoliths; 5 \u0026ndash; CAL and MIX; 3 \u0026ndash; LIP and MIX; 15 \u0026ndash; only CAL; 2 \u0026ndash; only LIP. Discussing the type of regulation of protein, only 1 protein was up-regulated among all of the samples, 24 proteins were down-regulated and 8 proteins had varied type of regulation. Most of the proteins were described above. Alpha-amylase 1A (AMY1A) has ability to binding of calcium, especially in saliva [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Zinc-alph-2-glycoprotin (AZGP1) degrades of lipids [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Lactotransferrin (LTF) was described as a crucial part in the NETs formation. It has antimicrobial activity and it depends on the extracellular cation concentration. LTF was also reported as a factor responsible for decreasing inflammatory processes, bacterial growth and biofilm development in saliva. Lactotransferrin is also established as a biomarker of salivary gland pathological states [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan additionalcitationids=\"CR78 CR79 CR80\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Example of protein with affinity to calcium ions is Matrix Gla protein (MGP) [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Mucin-7 (MUC7) takes part in the inflammatory processes, especially in saliva [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Leukocyte elastase inhibitor (SERPINB1) is another protein responsible for immune response in the presence of pathogens [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] There is also protein, Stomatin (STOM), which regulates the activity of ion channels in membranes [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Annexin A5 (ANXA5), as calcium-dependent protein, binds phospholipids and expose them on the cell surface during apoptosis [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Another protein playing crucial role in the activation of immune system in the presence of bacteria is Bactericidal permeability-increasing protein (BPI) [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Cathelicidin antimicrobial peptide (CAMP) and Immunoglobulin lambda constant 2 (IGLC2) are also responsible for immune response in saliva [\u003cspan additionalcitationids=\"CR91\" citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Lactoperoxidase (LPO) is secreted by the salivary glands or epithelial cells in the oral cavity and it produces antimicrobial agents by combining thiocyanate and hydrogen peroxide [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe final enrichment analysis confirmed previous conclusions (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). Analysing Biological Process Gene Ontology terms we can conclude, that the quantified and altered proteins, found in sialolithiasis, are mainly associated with the activity of immune system, what the most often depends on the presence of pathogens, such as bacteria. Focusing on Molecular Function Gene Ontology database, the main conclusion is fact, that in the case of pathological biocalcification the activity of endopeptidases may be disturbed and it may influence the balance of lipids. According to Cellular Component Gene Ontology terms, described proteins are also connected to extracellular regions \u0026ndash; because of the presence of bacteria and during immune response, the neutrophil extracellular traps are formed. These structures are also crucial factors for formation of salivary stones, according to one of the theories.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIdentification of bacterial proteins in both saliva and sialolith samples indicates that bacteria may contribute to the formation of salivary deposits. Although the overall salivary microbiome of patients with sialolithiasis is largely similar to that of healthy donors, a broader diversity of bacterial species is detected within sialoliths. Notably, several species are shared between sialoliths and saliva from both patient and control groups, suggesting close interactions among these microbiomes and a potential role of bacteria in sialolith formation.\u003c/p\u003e \u003cp\u003eDuring this study we wanted to check the applicability of saliva as reference sample in the quantitative proteomic analysis of sialoliths. The various comparisons between saliva collected form healthy donors and saliva collected form patients with sialolithiasis showed, that there are no statistically significant differences between these 2 samples. However, the quantification of proteins in salivary stones with any saliva sample brought many potential protein biomarkers, involved in calcification process leading to sialoliths formation.\u003c/p\u003e \u003cp\u003eSecond part was verification of previously established protein profiles of different types of sialoliths: calcified (CAL), lipid (LIP) and mixed (MIX). That study used pooled sialolith samples as reference and an experiment conducted in the same way now showed different results – only some part of quantified proteins was the same. This fact is reasonable, because the protein composition of reference sample depends on the protein composition of all pooled samples, so because it was set of different samples, the proteome of control sample was also different. Because of these differences, it was impossible to classify the sialoliths based only on the protein profiles established earlier. To make this more feasible, the spectroscopic studies should be repeated and, based on the results, the proteomic profiling of sialoliths should be done again, but with more repeatable reference sample, for example, saliva collected form healthy donors.\u003c/p\u003e \u003cp\u003eDespite the lack of differences between the compared saliva samples, we selected set of 33 proteins involved in the formation of salivary stones. They are high confident proteins, which were quantified and described also during previous study, where the different salivary stone types were profiled. Enrichment analysis of these proteins confirmed our previously drawn conclusions, that sialolithiasis is associated with the presence of bacteria, activity of immune system, formation of neutrophils cellular traps and altering the levels of calcium or lipids.\u003c/p\u003e "},{"header":"METHODOLOGY","content":"\u003ch2\u003eCollecting samples\u003c/h2\u003e\u003cp\u003eAll salivary stone and saliva samples were collected from patients under the care of the Department of Otolaryngology at the Medical University of Gdańsk. Patients were included in the study only after signing the necessary written consent and approval by the Independent Bioethics Commission at the Medical University of Gdańsk. All methods were performed in accordance with the relevant guidelines and regulations. Process of collecting the salivary stone samples was the same as described earlier [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], optimized and standardized according to the applicable routine protocol from the Clinic of Otolaryngology with the Department of Oral and Maxillofacial Surgery at The University Clinical Centre in Gdańsk. The saliva samples were collected before surgery by spitting into a sterile falcon tube (20ml of saliva). The patient could not eat and clean their teeth 1 hour before ordering; besides, before the spitting patient was washing his oral cavity with water for 1 minute. The proteolysis inhibitor was added to prevent the salivary proteins from proteolysis (water with trifluoroacetic acid). Secured saliva samples were stored at 80°C for further experiments and then transported to the Intercollegiate Faculty of Biotechnology of the University of Gdańsk and Medical University of Gdańsk on dry ice. Saliva samples from healthy donors were collected from faculty members who did not show signs of sialolithiasis or other salivary glands dysfunctions. Sialoliths of submandibular origin were removed during endoscopic, transoral or transcervical surgery. After that, the salivary stone samples were washed with use buffer (25 mM NH\u003csub\u003e4\u003c/sub\u003eHCO\u003csub\u003e3\u003c/sub\u003e) and then stored in sterile falcon tubes at 80°C for further experiments. The pictures of most of salivary stone samples are included (Supplementary File 1).\u003c/p\u003e\u003ch3\u003eProtein extraction from salivary stones\u003c/h3\u003e\u003cp\u003eThe used approaches of processing the sialolith samples were based on protocols described in previous publications [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. To perform the extraction of proteins from sialolith samples, the first step was crushing the salivary stones into the powder. It was served using mortar. To extract as much proteins as possible several lysis buffer, sample amount and sonication condition combinations were tested earlier and the most optimal were chosen. Sonication part was inspired by one of the papers [\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e]. Finally, 50mg of powdered sialolith was treated with 250µl of lysis buffer (3% SDS, 100 mM Tris– HCl pH 8,0, 50 mM DTT). First sample portion was treated in standard way: after adding the lysis buffer the samples were incubated at 95°C for 15 min with mixing, centrifugated and then the supernatant was collected. Second sample portion was processed using sonicator (Q700, Sound Enclosure, Cup Horn \u0026amp; Chiller, QSonica) for 15 min (cycle: 15s ON/5s OFF, amplitude: 75%, 20°C). Then, the samples were incubated at 95°C for 15 min with mixing and after that they were again sonicated under the same conditions as earlier. After centrifugation supernatant was collected. The concentration of both supernatants was measured and equally amounts of proteins were combined for each sample.\u003c/p\u003e\u003ch2\u003eProtein extraction from saliva\u003c/h2\u003e\u003cp\u003eSimilar approach was used to process the saliva samples. Again, the most optimal protocol was chosen after testing several combinations. Finally, 50µl of saliva was treated with 250µl of lysis buffer (1% SDS, 100 mM Tris– HCl pH 8,0, 50 mM DTT). Again, first sample portion was treated in standard way, second sample portion was processed using sonicator, as described above, for 15 min (cycle: 15s ON/5s OFF, amplitude: 50%, 20°C). The concentration of both supernatants was measured and equally amounts of proteins were combined for each sample.\u003c/p\u003e\u003ch2\u003eDigestion of salivary stone and saliva samples\u003c/h2\u003e\u003cp\u003eFor digestion part, protocol based on standard FASP and sonication-aided FASP was used [\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e95\u003c/span\u003e]. First portion of extracted proteins was digested according standard FASP approach on a 10 kDa membrane. Digestion lasted overnight. Second portion of extracted proteins was processed with sonication, also on a 10 kDa membrane. Trypsin was added and the samples were sonicated for 15 min (cycle: 15s ON/5s OFF, amplitude: 50%) at 37°C. After that, digestion was done. The concentration of both peptide fractions was measured and equally amounts of peptides were combined for each sample and prepared for MS analysis by final clean-up on C18 (exchange disks 3 M EmporeTM) StageTips according to described protocol [\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eConstruction of the spectral library\u003c/h2\u003e\u003cp\u003eTo maximize the number of quantified proteins the final spectral library intended for SWATH-MS analysis consisted of various spectra:\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSingle DDA spectra for each salivary stone and saliva sample described in this work;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSingle DDA spectra for each salivary stone sample described and recorded during our previous work [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDDA spectra for pooled sialolith protein fractions after separation in gel (SageELF) and digested according to FASP protocol, described and recorded during our previous work [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDDA spectra for peptide fractions after FASP digestion and chromatographic separation in alkaline pH; described and recorded during our previous work [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003ch2\u003eQualitative (DDA) LC-MS/MS analysis\u003c/h2\u003e\u003cp\u003eThis part was performed as described earlier [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. LC–MS/MS analysis was performed on Triple-TOF 5600 + mass spectrometer (AB Sciex LLC, Framingham, MA, USA) connected with Ekspert MicroLC 200 Plus System (Eksigent, Dublin, CA, USA). The Analyst TF 1.7.1 software (SCIEX) controlled the whole system. The chromatographic gradient for each MS run was 11–42% B (A: H2O + 0,1% FA; B: 100% ACN + 0,1% FA) in 60 min. ChromXP C18CL column (3µm, 120Å, 150×0.3mm) was used to perform the chromatographic separation. The spectra were registered in information dependent acquisition (IDA) mode to perform qualitative analysis and build the library. Each cycle comprised precursor spectra accumulation in 100ms in the range of 400–1200 m/z followed by top 20 precursor ion spectra accumulation in 50ms in the range of 100–1800 m/z, resulting in a total cycle time of 1.15s. Formerly fragmented precursor ions were dynamically excluded.\u003c/p\u003e\u003ch2\u003eQuantitative (DIA) LC-MS/MS analysis\u003c/h2\u003e\u003cp\u003eThis part was performed almost the same as described earlier [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. The equalized frequency of precursor ions and coverage of the precursor mass range of 400–1200 m/z was used to construct the set of 25 transmission windows of variable width with SWATH® Variable Window Assay Calculator (AB Sciex LLC, Framingham, MA, USA). The collision energy for each window was calculated for + 2 to + 5 charged ions centered upon the window with a spread of five. The SWATH-MS survey scan was acquired in the range covered by constructed windows at the beginning of each cycle with an accumulation time of 50ms. Following SWATH-MS/MS spectra, product ion scans were collected in the range of 100 to 1800m/z in 39,995ms, which resulted in a total cycle time of 1,0999 s. Spectra were registered in 3 technical replications in data-independent acquisition (DIA) mode for each sample [\u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eLC-MS/MS data processing\u003c/h2\u003e\u003cp\u003eFirst, the DDA spectra for each clinical sample were analysed in PeaksSTUDIO software with the following settings: instrument: TripleTOF; fragmentation method: CID; acquisition: IDA; Parent Mass Error Tolerance: 15.0 ppm; Fragment Mass Error Tolerance: 0.05 Da; Precursor Mass Search Type: monoisotopic; Digestion: trypsin, Max Missed Cleavages: 2; Digest Mode: Specific; Peptide Length Range: 6–45; Fixed Modifications: Carbamidomethylation (+ 57.02); Variable Modifications: Formylation (+ 27.99) and Oxidation (M) (+ 15.99), Max Variable PTM Per Peptide: 2. The spectra were processed against the entire bacterial database with \u003cem\u003eoral cavity\u003c/em\u003e keyword filter (Uniprot, 16.01.2026).\u003c/p\u003e\u003cp\u003eIn terms of quantitative analysis, data were processed in PeakView 2.2 software (SCIEX), and DIA spectra were processed against the created sialoliths and saliva combined spectral library, which was constructed using ProteinPilot 4.5 software (Sciex; \u003cem\u003eHomo sapiens\u003c/em\u003e database, Uniprot, 23.11.2024) and all DDA spectra described in \u003cem\u003eConstruction of the spectral library\u003c/em\u003e part. After processing all of the clinical samples registered in DIA mode in PeakView software according to settings described by Lewandowska [\u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e], SWATH data were generated.\u003c/p\u003e\u003cp\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://proteomecentral.proteomexchange.org\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via the PRIDE partner repository [\u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e] with the dataset identifier PXD072871.\u003c/p\u003e\u003ch2\u003eStatistical and enrichment analysis\u003c/h2\u003e\u003cp\u003eThis part of analysis was performed based on the previous project [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. Qualitative data were processed in Excel. SWATH data from PeakView software were exported to MarkerView 1.2.1.1 software (Sciex). Data were normalized using the total area sums (TAS) approach. Then, the output table was exported to Perseus 2.1.1.0 software (MaxQuant) [\u003cspan class=\"CitationRef\"\u003e99\u003c/span\u003e] to perform statistical tests and calculate fold change (FC) values.\u003c/p\u003e\u003cp\u003eEnrichment analysis was performed, using STRING 12.0 [\u003cspan class=\"CitationRef\"\u003e100\u003c/span\u003e]. For data visualization BioRender [\u003cspan class=\"CitationRef\"\u003e101\u003c/span\u003e], Cytoscape 3.10.2 [\u003cspan class=\"CitationRef\"\u003e102\u003c/span\u003e], InteractiVenn [\u003cspan class=\"CitationRef\"\u003e103\u003c/span\u003e] and SRplot tool [\u003cspan class=\"CitationRef\"\u003e104\u003c/span\u003e] were used.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study protocol was approved by the Regional Bioethics Committee of Gdansk Medical University, Poland, with approval NKBBN/452/2019.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [98] partner repository with the dataset identifier\u0026nbsp;PXD072871.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe research was financed by The Small Grants - UGrants - start 2 programs, University of Gdańsk, Poland.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u0026nbsp;\u003c/strong\u003eNM – research concept, conducting experiments, analysing results, preparing and editing the manuscript; IM - conducting experiments, analysing results, preparing and editing the manuscript; DT, AS, KS, JS, BM \u0026nbsp; - research concept, collection of research material, revision of the manuscript; PC – research concept, supervision of the project and experiments, preparation, revision and proofreading of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSigismund, P.E., Zenk, J., Koch, M., Schapher, M., Rudes, M. \u0026amp; Iro, H. 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PLoS One 18, e0294236 (2023). https://doi.org/10.1371/journal.pone.0294236\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Proteomics, Saliva, Salivary stones, Biomarkers, Biocalcification, Sialolithiasis","lastPublishedDoi":"10.21203/rs.3.rs-8967608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8967608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe proteins associated with poorly described mechanism of sialolithiasis pathogenesis were previously described, but to increase credibility of these results and to discover new biomarkers of this disease it would be beneficial to verify the validity of optimized protocols during performing the quantitative analysis to establish the most reasonable reference sample. Previously established protocols were used to perform optimal protein extraction and digestion in saliva and salivary stone samples. Based on the DDA spectra the well-developed spectra library was created and then the DIA spectra were used to conduct relative quantitative proteomic analysis of saliva and sialoliths. The optimized workflows allowed to quantify the proteins in saliva and salivary stone samples. After statistical analysis it was possible to compare protein profiles of different saliva samples and sialolith samples, depending on the chosen reference sample. This study verified the applicability of saliva as reference sample in quantitative proteomic analysis of sialoliths, but at the same time no differences between saliva from healthy donors and saliva from patients with sialolithiasis were detected. It was possible to identify high confident proteins associated with pathological calcification leading to sialoliths formation.\u003c/p\u003e","manuscriptTitle":"Comparative proteomic analysis of saliva and salivary stones in sialolithiasis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 08:26:46","doi":"10.21203/rs.3.rs-8967608/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T04:51:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T13:59:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T20:42:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201517089608194602660542610106308309226","date":"2026-03-30T10:06:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337912289916798724410899103997438233191","date":"2026-03-14T16:42:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-12T16:02:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T15:52:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T14:34:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T16:30:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-04T10:20:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20f1fd14-663e-4648-8e3b-79b3ea9074d9","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":64513758,"name":"Biological sciences/Biochemistry"},{"id":64513759,"name":"Health sciences/Biomarkers"},{"id":64513760,"name":"Health sciences/Diseases"},{"id":64513761,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-04-22T04:55:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 08:26:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8967608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8967608","identity":"rs-8967608","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}