Probing the Salivary Proteome for Prognostic Biomarkers in Response to Non-Surgical Management of Periodontitis
Probing the Salivary Proteome for Prognostic Biomarkers in Response to Non-Surgical Management of Periodontitis
This study aims to investigate salivary proteome changes in periodontitis patients before and after non-surgical treatment. Ten systemically healthy and non-smoker individuals with stage III, grade C periodontitis underwent non-surgical periodontal treatment. Saliva was collected at baseline, and one and six months post-treatment. Whole-mouth plaque and gingival index, probing depth, bleeding on probing and clinical attachment loss were measured. The saliva proteome was investigated by label-free quantitative proteomics. Normalized protein intensities were measured and protein changes were modeled over time with significant protein regulation considered at false discovery rate (FDR)<0.05.
Study Population and Clinical Examination
A total of 10 systemically healthy and non-smoker individuals with stage III, grade C periodontitis were recruited at the Department of Periodontology, School of Dentistry, Aydın Adnan Menderes University, Aydın, Turkey.
Full-mouth periodontal assessment included measurements of probing depth, clinical attachment loss, the percentage of sites with bleeding on probing, gingival index, and plaque index at baseline (T0) and one month (T1) and six months (T6) post-treatment. All measurements were obtained from 6 sites per tooth excluding third molars using a manual periodontal probe (William's periodontal probe, Hu-Friedy, Chicago, IL). The alveolar bone resorption was assessed on the digital panoramic radiograph in each participant. All clinical measurements were performed by an experienced periodontist (B.A.).
Diagnosis of stage III, grade C periodontitis was performed according to the criteria proposed by the 2017 International Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions.
Treatment
Conventional quadrant-wise scaling and root planing (SRP) was conducted at baseline, after saliva collection and periodontal assessment, starting with the upper right quadrant and continuing clockwise over four visits at weekly intervals. SRP was performed under local anesthesia by the same periodontist (B.A.) by use of ultrasonic instruments (Mini Piezon, EMS, Nyon, CH) and manual periodontal curettes (Gracey curets, scaler Hu-Friedy, Chicago, IL). All present teeth were instrumented until the root surface was visually and tactilely clean and smooth. Self-performed plaque control measures consisted of toothbrushing using the modified Bass technique with a medium toothbrush and a regular toothpaste with fluoride twice a day and interdental cleaning using dental floss and/or interdental brushes once a day. Follow-up saliva sampling and periodontal assessments were performed at T1 and T6 after completing SRP.
Saliva sampling
Unstimulated whole saliva was obtained from the participants in the morning between 8:00 am - 10:00 am at T0, T1 and T6. All patients were requested to abstain from eating, drinking and all oral hygiene procedures for 2 h prior to sampling. Patients were first asked to rinse their mouth with tap water and wait for 5 min to avoid sample dilution. Unstimulated saliva was collected over a 5-min period; patients were instructed to sit with their head tilted forward to encourage passive drooling, expectorating into a sterile universal polypropylene container tube. After sampling, samples were immediately transferred to refrigerated storage (4 °C), aliquoted and frozen at -80 °C until required.
Label-free quantitative proteomic analysis
Saliva supernatant samples collected at T0, T1 and T6 (n = 30) were analyzed using a label-free quantitative (LFQ) proteomic approach.
Sample preparation
Samples were prepared by using a commercial iST Kit (PreOmics, Germany) with an updated version of the protocol. The protein concentration was estimated using the Qubit® Protein Assay Kit (Life Technologies, Zurich, Switzerland). For each sample, 50 µg of protein were transferred to the cartridge and digested by adding 50 µL of the 'Digest' solution. After 60 minutes of incubation at 37 °C the digestion was stopped with 100 µL of Stop solution. The solutions in the cartridge were removed by centrifugation at 3800 g, while the peptides were retained by the iST-filter. Finally, the peptides were washed, eluted, dried and re-solubilized in 20 µL of injection buffer (3% acetonitrile, 0.1% formic acid).
Liquid chromatography-mass spectrometry analysis
Mass spectrometry analysis was performed on a Q Exactive HF-X mass spectrometer (Thermo Scientific) equipped with a Digital PicoView source (New Objective) and coupled to a M-Class UPLC (Waters). Solvent composition at the two channels was 0.1% formic acid for channel A and 0.1% formic acid, 99.9% acetonitrile for channel B. For each sample, 2 μL of peptides were loaded on a commercial MZ Symmetry C18 Trap Column (100Å, 5 µm, 180 µm x 20 mm, Waters) followed by nanoEase MZ C18 HSS T3 Column (100Å, 1.8 µm, 75 µm x 250 mm, Waters). The peptides were eluted at a flow rate of 300 nL/min by a gradient from 8 to 27% B in 85 min, 35% B in 5 min and 80% B in 1 min. Samples were acquired in a randomized order. The mass spectrometer was operated in data-dependent mode (DDA), acquiring a full-scan MS spectra (350 - 1'400 m/z) at a resolution of 120'000 at 200 m/z after accumulation to a target value of 3'000'000, followed by HCD (higher-energy collision dissociation) fragmentation on the twenty most intense signals per cycle. HCD spectra were acquired at a resolution of 15'000 using a normalized collision energy of 28 and a maximum injection time of 22 ms. The automatic gain control (AGC) was set to 100'000 ions. Charge state screening was enabled. Singly, unassigned, and charge states higher than seven were rejected. Only precursors with intensity above 250'000 were selected for MS/MS. Precursor masses previously selected for MS/MS measurement were excluded from further selection for 30 s, and the exclusion window was set at 10 ppm. The samples were acquired using internal lock mass calibration on m/z 371.1012 and 445.1200.
Protein identification and quantification
The acquired raw MS data was processed by MaxQuant (version 1.6.2.3), followed by protein identification using the integrated Andromeda search engine. The analysis was run on the local laboratory information management system (LIMS). Spectra were searched against a database containing the Uniprot human reference proteome (taxonomy 9606, canonical version from 20180703) and the Human oral Microbiome Database (http://www.homd.org/, version annotated genomes 460+, 20180702), concatenated to their reversed decoyed fasta database and common protein contaminants. Carbamidomethylation of cysteine was set as fixed modification, while methionine oxidation and N-terminal protein acetylation were set as variable. Enzyme specificity was set to trypsin/P, allowing a minimal peptide length of 7 amino acids and a maximum of two missed cleavages. MaxQuant Orbitrap default search settings were used. The maximum false discovery rate (FDR) was set to 0.01 for peptides and 0.05 for proteins. LFQ was enabled and a two-minute window for matches between runs was applied. In the MaxQuant experimental design template, each file is kept separate in the experimental design to obtain individual quantitative values.
Protein intensities and regulation
A set of R functions implemented in the R package prolfqua was used to perform the differential expression analysis. The peptide intensities reported by MaxQuant were log2-transformed and then z-transformed so that the sample means and variances were equal. Next, normalized protein intensities were estimated from the peptide intensities using Tukey's median polish. Protein log2-fold changes were computed based on normalized protein intensities. A linear model with two factors (time and patient) was fitted to each protein, and group differences were estimated and tested based on the model parameters. The variance estimates were moderated using the empirical Bayes approach, which exploits the parallel structure of the high throughput experiment, which increases the statistical power of the Null hypothesis significance test. Finally, the p-values were adjusted using the Benjamini and Hochberg procedure to obtain the FDRs. Differentially regulated proteins between the time points (T1 vs T0, T6 vs T0 and T6 vs T1) were considered at FDR <0.05. Principal component analysis was performed for visualization purposes using the R function 'prcomp'.
Pathway analysis
Differentially regulated human proteins (FDR <0.05) were further subjected to the QIAGEN Ingenuity Pathway Analysis (IPA) application (QIAGEN Inc., https://digitalinsights.qiagen.com/IPA) to investigate which diseases and biological processes were affected by regulatory changes in our proteome dataset. The analysis is based on expected causal effects between regulated proteins and associated diseases or functions. IPA is a powerful bioinformatic analysis tool, based on QIAGEN's Knowledge Base combining scientific and biomedical information from journal articles and databases, that allows for interpretation of a wide range of omics expression data and predictions of potential regulatory networks and causal relationships. A core analysis was performed on the 12th of April 2022 based on log2-fold change and FDR data for T1 vs T0 and T6 vs T0, and the top regulated disease and biological processes ranked by FDR were identified. Furthermore, a z-score prediction algorithm was applied and disease or functional activities were categorized as significantly increased (positive z-scores) or decreased (negative z-scores) for z-scores of ≥2 or ≤-2, respectively and presented as heatmaps with different square sizes (FDR) and colors (z-scores). Larger squares indicate a more significant overlap between the regulated proteins in the dataset and a specific disease or function, while square colors reflect the direction of change for a disease or function (orange: increased, blue: decreased). Proteins associated with a specific disease or function were presented as network charts indicating protein function, protein regulation (up- or down-regulated), and the predicted type of association/ interaction (indirect or direct, activation or inhibition).
Statistical Analysis
A set of R functions implemented in the R package prolfqua was used to perform the differential expression analysis. The peptide intensities reported by MaxQuant were log2-transformed and then z-transformed so that the sample means and variances were equal. Next, normalized protein intensities were estimated from the peptide intensities using Tukey's median polish. Protein log2-fold changes were computed based on normalized protein intensities. A linear model with two factors (time and patient) was fitted to each protein, and group differences were estimated and tested based on the model parameters. The variance estimates were moderated using the empirical Bayes approach, which exploits the parallel structure of the high throughput experiment, which increases the statistical power of the Null hypothesis significance test. Finally, the p-values were adjusted using the Benjamini and Hochberg procedure to obtain the FDRs. Differentially regulated proteins between the time points (T1 vs T0, T6 vs T0 and T6 vs T1) were considered at FDR <0.05. Principal component analysis was performed for visualization purposes using the R function 'prcomp'.
Inclusion Criteria:
Exclusion Criteria: