Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease
2018; Elsevier BV; Volume: 17; Issue: 7 Linguagem: Inglês
10.1074/mcp.ra118.000718
ISSN1535-9484
AutoresNagihan Bostancı, Nathalie Selevsek, Witold Wolski, Jonas Grossmann, Kai Bao, Åsa Wåhlander, Christian Trachsel, Ralph Schlapbach, Veli Özgen Öztürk, Beral Afacan, Gülnur Emingil, Georgios N. Belibasakis,
Tópico(s)Bone and Dental Protein Studies
ResumoPeriodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. They affect the tooth-supporting tissues with multiple ramifications on life quality. Their early diagnosis is still challenging, due to lack of appropriate molecular diagnostic methods. Saliva offers a non-invasively collectable reservoir of clinically relevant biomarkers, which, if utilized efficiently, could facilitate early diagnosis and monitoring of ongoing disease. Despite several novel protein markers being recently enlisted by discovery proteomics, their routine diagnostic application is hampered by the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. Here we carried out a pipeline of two proteomic platforms; firstly, we applied open ended label-free quantitative (LFQ) proteomics for discovery in saliva (n = 67, including individuals with health, gingivitis, and periodontitis), followed by selected-reaction monitoring (SRM)-targeted proteomics for validation in an independent cohort (n = 82). The LFQ platform led to the discovery of 119 proteins with at least 2-fold significant difference between health and disease. The 65 proteins chosen for the subsequent SRM platform included 50 functionally related proteins derived from the significantly enriched processes of the LFQ data, 11 from literature-mining, and four house-keeping ones. Among those, 60 were reproducibly quantifiable proteins (92% success rate), represented by a total of 143 peptides. Machine-learning modeling led to a narrowed-down panel of five proteins of high predictive value for periodontal diseases with maximum area under the receiver operating curve >0.97 (higher in disease: Matrix metalloproteinase-9, Ras-related protein-1, Actin-related protein 2/3 complex subunit 5; lower in disease: Clusterin, Deleted in Malignant Brain Tumors 1). This panel enriches the pool of credible clinical biomarker candidates for diagnostic assay development. Yet, the quantum leap brought into the field of periodontal diagnostics by this study is the application of the biomarker discovery-through-verification pipeline, which can be used for validation in further cohorts. Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. They affect the tooth-supporting tissues with multiple ramifications on life quality. Their early diagnosis is still challenging, due to lack of appropriate molecular diagnostic methods. Saliva offers a non-invasively collectable reservoir of clinically relevant biomarkers, which, if utilized efficiently, could facilitate early diagnosis and monitoring of ongoing disease. Despite several novel protein markers being recently enlisted by discovery proteomics, their routine diagnostic application is hampered by the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. Here we carried out a pipeline of two proteomic platforms; firstly, we applied open ended label-free quantitative (LFQ) proteomics for discovery in saliva (n = 67, including individuals with health, gingivitis, and periodontitis), followed by selected-reaction monitoring (SRM)-targeted proteomics for validation in an independent cohort (n = 82). The LFQ platform led to the discovery of 119 proteins with at least 2-fold significant difference between health and disease. The 65 proteins chosen for the subsequent SRM platform included 50 functionally related proteins derived from the significantly enriched processes of the LFQ data, 11 from literature-mining, and four house-keeping ones. Among those, 60 were reproducibly quantifiable proteins (92% success rate), represented by a total of 143 peptides. Machine-learning modeling led to a narrowed-down panel of five proteins of high predictive value for periodontal diseases with maximum area under the receiver operating curve >0.97 (higher in disease: Matrix metalloproteinase-9, Ras-related protein-1, Actin-related protein 2/3 complex subunit 5; lower in disease: Clusterin, Deleted in Malignant Brain Tumors 1). This panel enriches the pool of credible clinical biomarker candidates for diagnostic assay development. Yet, the quantum leap brought into the field of periodontal diagnostics by this study is the application of the biomarker discovery-through-verification pipeline, which can be used for validation in further cohorts. Periodontal diseases are oral biofilm induced chronic inflammatory diseases of the tooth-supporting (periodontal) tissues. Despite major improvements in oral hygiene practices in industrialized countries, severe periodontitis remains the sixth-most prevalent chronic disease worldwide, affecting almost 11.5% of many populations (1.Kassebaum N.J. Bernabe E. Dahiya M. Bhandari B. Murray C.J. Marcenes W. Global burden of severe periodontitis in 1990–2010: a systematic review and meta-regression.J. Dental Res. 2014; 93: 1045-1053Crossref PubMed Scopus (1109) Google Scholar, 2.Albandar J.M. Brunelle J.A. Kingman A. Destructive periodontal disease in adults 30 years of age and older in the United States, 1988–1994.J. Periodontol. 1999; 70: 13-29Crossref PubMed Scopus (569) Google Scholar). This cluster of oral diseases do not only affect the tooth-supporting tissues but also the other body parts by contributing to the development of life threating conditions, namely, cardiovascular disease or stroke (3.Papapanou P.N. Systemic effects of periodontitis: lessons learned from research on atherosclerotic vascular disease and adverse pregnancy outcomes.Int. Dent. J. 2015; 65: 283-291Crossref PubMed Scopus (69) Google Scholar, 4.Ryden L. Buhlin K. Ekstrand E. de Faire U. Gustafsson A. Holmer J. Kjellstrom B. Lindahl B. Norhammar A. Nygren A. Nasman P. Rathnayake N. Svenungsson E. Klinge B. Periodontitis increases the risk of a first myocardial infarction: a report from the PAROKRANK Study.Circulation. 2016; 133: 576-583Crossref PubMed Scopus (154) Google Scholar). Therefore, identifying early and abolishing the onset of these diseases is highly desirable. Similar to the other chronic diseases in humans, there are still considerable challenges in diagnosis and classification for the cases of different forms of periodontal diseases and current diagnosis is based on subjective indices, mainly evaluating the past disease (5.Chapple I.L. Periodontal diagnosis and treatment–where does the future lie?.Periodontol. 2000. 2009; 51: 9-24Crossref PubMed Scopus (48) Google Scholar). The poor performance of clinical tools and unpredictability in the progression of the disease has led to a search for new, more accurate biomarkers in oral biofluids for periodontal disease screening, classification monitoring, and management since 1960s (6.Bao K. Bostanci N. Selevsek N. Thurnheer T. Belibasakis G.N. Quantitative proteomics reveal distinct protein regulations caused by Aggregatibacter actinomycetemcomitans within subgingival biofilms.PloS one. 2015; 10: e0119222Crossref PubMed Scopus (34) Google Scholar, 7.Belibasakis G.N. Bostanci N. 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Salivary matrix metalloproteinase-8 and -9 and myeloperoxidase in relation to coronary heart and periodontal diseases: a subgroup report from the PAROKRANK Study (Periodontitis and Its Relation to Coronary Artery Disease).PloS one. 2015; 10: e0126370Crossref PubMed Scopus (35) Google Scholar, 14.Hu S. Loo J.A. Wong D.T. Human body fluid proteome analysis.Proteomics. 2006; 6: 6326-6353Crossref PubMed Scopus (431) Google Scholar). Early studied biomarkers in saliva were more etiology oriented and ranged from specific bacteria or their secreted products to host immune markers or tissue lysis products (15.Kaufman E. Lamster I.B. Analysis of saliva for periodontal diagnosis–a review.J. Clin. Periodontol. 2000; 27: 453-465Crossref PubMed Scopus (258) Google Scholar). A few biomarkers have been marketed for chair-side use but the most have disappeared from the market because of their low specificity (16.Nomura Y. Tamaki Y. Tanaka T. Arakawa H. Tsurumoto A. Kirimura K. Sato T. Hanada N. Kamoi K. Screening of periodontitis with salivary enzyme tests.J. Oral Sci. 2006; 48: 177-183Crossref PubMed Scopus (74) Google Scholar, 17.Hemmings K.W. Griffiths G.S. Bulman J.S. Detection of neutral protease (Periocheck) and BANA hydrolase (Perioscan) compared with traditional clinical methods of diagnosis and monitoring of chronic inflammatory periodontal disease.J. Clin. Periodontol. 1997; 24: 110-114Crossref PubMed Scopus (23) Google Scholar, 18.Bretz W.A. Eklund S.A. Radicchi R. Schork M.A. Schork N. Schottenfeld D. Lopatin D.E. Loesche W.J. The use of a rapid enzymatic assay in the field for the detection of infections associated with adult periodontitis.J. Public Health Dent. 1993; 53: 235-240Crossref PubMed Scopus (9) Google Scholar). A single protein marker is less likely to reliably detect early periodontal disease or to provide a differential diagnosis between different forms of the disease (19.Kinney J.S. Morelli T. Braun T. Ramseier C.A. Herr A.E. Sugai J.V. Shelburne C.E. Rayburn L.A. Singh A.K. Giannobile W.V. Saliva/pathogen biomarker signatures and periodontal disease progression.J. Dental Res. 2011; 90: 752-758Crossref PubMed Scopus (138) Google Scholar). A better approach is to aim for a panel of related markers for conclusive prediction. We have demonstrated earlier that label-free quantitative (LFQ) 1The abbreviations used are: LFQ, label-free quantitative; ACN, acetonitrile; ACM3, muscarinic acetylcholine receptor M3; AP, aggressive periodontitis; AUC, area under curve; A2M, alpha-2-macroglobulin; ARPC5, actin-related protein 2/3 complex subunit 5; BOP, bleeding on probing; CAL, clinical attachment loss; CDC42, cell division control protein 42 homolog; CID, collision-induced dissociation; CLUS, clusterin; CP, chronic periodontitis; DIA, data independent analysis; DMBT1, deleted in malignant brain tumors 1; EDTA, ethylenediaminetetraacetic acid; ENOA, enolase; FDR, false discovery rate; FIBB, fibrinogen beta chain; H, periodontal health; HSP27, heat shock 27 kDa protein; G, gingivitis; GO, Gene Ontology; GCF, gingival crevicular fluid; JAK-STAT, Janus kinase-signal transducer and activator of transcription; IL, Interleukin; IL-1RN, Interleukin-1 receptor antagonist protein; LC, liquid chromatography; MMPs, matrix metalloproteinass; MRM, multiple reaction monitoring; MS/M, tandem mass spectrometry; PD, probing depth; PI, plaque index; PMNs, polymorphonuclear cells; PRM, parallel reaction monitoring; RAP1A, Ras-related protein Rap-1; ROC, receiver operating characteristic; SLC4A1, solute carrier family 4 member 1; SRM, selected reaction monitoring; SWATH, sequential window acquisition of all theoretical spectra. 1The abbreviations used are: LFQ, label-free quantitative; ACN, acetonitrile; ACM3, muscarinic acetylcholine receptor M3; AP, aggressive periodontitis; AUC, area under curve; A2M, alpha-2-macroglobulin; ARPC5, actin-related protein 2/3 complex subunit 5; BOP, bleeding on probing; CAL, clinical attachment loss; CDC42, cell division control protein 42 homolog; CID, collision-induced dissociation; CLUS, clusterin; CP, chronic periodontitis; DIA, data independent analysis; DMBT1, deleted in malignant brain tumors 1; EDTA, ethylenediaminetetraacetic acid; ENOA, enolase; FDR, false discovery rate; FIBB, fibrinogen beta chain; H, periodontal health; HSP27, heat shock 27 kDa protein; G, gingivitis; GO, Gene Ontology; GCF, gingival crevicular fluid; JAK-STAT, Janus kinase-signal transducer and activator of transcription; IL, Interleukin; IL-1RN, Interleukin-1 receptor antagonist protein; LC, liquid chromatography; MMPs, matrix metalloproteinass; MRM, multiple reaction monitoring; MS/M, tandem mass spectrometry; PD, probing depth; PI, plaque index; PMNs, polymorphonuclear cells; PRM, parallel reaction monitoring; RAP1A, Ras-related protein Rap-1; ROC, receiver operating characteristic; SLC4A1, solute carrier family 4 member 1; SRM, selected reaction monitoring; SWATH, sequential window acquisition of all theoretical spectra. mass spectrometry methods are able to facilitate characterization and concurrent quantitative analysis of the proteome in periodontal health and disease (10.Bostanci N. Heywood W. Mills K. Parkar M. Nibali L. Donos N. Application of label-free absolute quantitative proteomics in human gingival crevicular fluid by LC/MS E (gingival exudatome).J. Proteome Res. 2010; 9: 2191-2199Crossref PubMed Scopus (105) Google Scholar, 20.Bostanci N. Ramberg P. Wahlander A. Grossman J. Jonsson D. Barnes V.M. Papapanou P.N. Label-free quantitative proteomics reveals differentially regulated proteins in experimental gingivitis.J. Proteome Res. 2013; 12: 657-678Crossref PubMed Scopus (51) Google Scholar). Although the non-targeted, shotgun proteomic workflows are considerably successful in the discovery of novel candidate markers and in generating hypotheses for periodontal diseases, no direct effect on improved diagnostic capacity has been demonstrated. This goes in line with the fact that no protein biomarker has been incorporated into the daily dental practice or in a clinical assay, despite more than 600 proteins have been linked to the disease by proteomics work (21.Bostanci N. Bao K. Contribution of proteomics to our understanding of periodontal inflammation.Proteomics. 2017; 17Crossref PubMed Scopus (35) Google Scholar). The main reason is the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. Targeted mass spectrometry (MS) methods are at the fore front for accurate (high specificity and sensitivity) measurement of dozens of proteins simultaneously in complex biological samples decreasing the requirements for (individual target) antibody-based assays (22.Aebersold R. Bensimon A. Collins B.C. Ludwig C. Sabido E. Applications and developments in targeted proteomics: From SRM to DIA/SWATH.Proteomics. 2016; 16: 2065-2067Crossref PubMed Scopus (43) Google Scholar, 23.Aebersold R. Burlingame A.L. Bradshaw R.A. Western blots versus selected reaction monitoring assays: time to turn the tables?.Mol. Cell. Proteomics. 2013; 12: 2381-2382Abstract Full Text Full Text PDF PubMed Scopus (204) Google Scholar). The separation and detection methodology, termed selected reaction monitoring (SRM) or multiple reaction monitoring (MRM), has matured into a robust technology for reproducible and reliable quantification of protein panels in complex sample backgrounds. These advancements within the field has led to an increasing interest in using liquid chromatography (LC)-MS as a primary biomarker discovery and validation platform (24.Ebhardt H.A. Root A. Sander C. Aebersold R. Applications of targeted proteomics in systems biology and translational medicine.Proteomics. 2015; 15: 3193-3208Crossref PubMed Scopus (133) Google Scholar). Despite numerous reports describing the fast-growing application of SRM-based workflows for quantification of target peptides (proteins) in plasma (25.Addona T.A. Abbatiello S.E. Schilling B. Skates S.J. Mani D.R. Bunk D.M. Spiegelman C.H. Zimmerman L.J. Ham A.J. Keshishian H. Hall S.C. Allen S. Blackman R.K. Borchers C.H. Buck C. Cardasis H.L. Cusack M.P. Dodder N.G. Gibson B.W. Held J.M. Hiltke T. Jackson A. Johansen E.B. Kinsinger C.R. Li J. Mesri M. Neubert T.A. Niles R.K. Pulsipher T.C. Ransohoff D. Rodriguez H. Rudnick P.A. Smith D. Tabb D.L. Tegeler T.J. Variyath A.M. Vega-Montoto L.J. Wahlander A. Waldemarson S. Wang M. Whiteaker J.R. Zhao L. Anderson N.L. Fisher S.J. Liebler D.C. Paulovich A.G. Regnier F.E. Tempst P. Carr S.A. 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Multiplexed panel of precisely quantified salivary proteins for biomarker assessment.Proteomics. 2017; 17Crossref Scopus (11) Google Scholar) and there has been no application to the field of periodontics. We have followed two main mass-spectrometry guided strategies to identify biomarkers of periodontal diseases in human saliva. First, the discovery study with cross-sectional case-control design (n = 67) was conducted to dissect comparative saliva proteome in (1) periodontal health (2) during inflammatory but not destructive disease stage (gingivitis) (3) during advanced disease stage in healthy young individuals (generalized aggressive periodontitis) (4) during advanced but chronic disease stage in older individuals (generalized chronic periodontitis) by LFQ. Second, we have conducted multiplex LC-SRM assays with an independent cohort (n = 82) in order to qualify or validate the identified candidate markers by the discovery approach. The whole saliva samples were obtained from 67 systemically healthy subjects (age range 20–64 years) consisting of patients with generalized chronic periodontitis (CP), generalized aggressive periodontitis (AP), gingivitis (G) and individuals with periodontal health (H). The use of humans for study satisfied the requirements of the Ege University Institutional Review Board (Ethics number 16–12.1/16) and was conducted in accordance with the guidelines of the World Medical Association Declaration of Helsinki. It is confirmed that this cross-sectional case-control study conforms to STROBE guidelines for observational studies. Complete medical and dental histories were obtained from all participants. Systemic exclusion criteria were the presence of cardiovascular and respiratory diseases, diabetes mellitus, HIV infection, systemic inflammatory conditions or non-plaque-induced oral inflammatory conditions, immunosuppressive chemotherapy, and current pregnancy or lactation or smoking. None of the patients had taken medication such as antibiotics or anti-inflammatory drugs that could affect their periodontal status for at least 6 months before the study. Patients eligible for the study returned to the clinic for clinical measurement screening one-week after being pre-screened. Before being enrolled in the study, participants provided written and informed consent for use of their saliva samples and clinical data for scientific research purposes. The clinical periodontal indices including probing depth (PD), clinical attachment loss (CAL), plaque index (PI) and bleeding on probing (BOP) were recorded by a manual periodontal probe by a trained and calibrated examiner (V.Ö.Ö.). The extent and severity of alveolar bone support was evaluated radiographically in each patient. The participants were classified into four groups based on their periodontal conditions according to the criteria proposed by the 1999 International Workshop for a Classification of Periodontal Diseases and Conditions (30.Armitage G.C. Development of a classification system for periodontal diseases and conditions.Ann. Periodontol. 1999; 4: 1-6Crossref PubMed Scopus (3519) Google Scholar). The AP group included 17 patients with ≥16 teeth. The patients had a non- contributory medical history and demonstrated with an early age of clinical manifestations with a generalized pattern of rapid attachment loss and bone destruction disproportionate to the magnitude of local etiological factors. Additionally, self-reported family history of periodontitis was a strong indicator of the diagnosis. These individuals had minimum of CAL greater 5 mm and PD greater 6 mm on eight or more teeth; at least three of these were other than central incisors or first molars. Radiographic bone loss was above 30% of root length affecting more than 3 permanent teeth other than first molars and incisors. The CP group (n = 17) included individuals who had a minimum four non-adjacent teeth with sites with CAL greater 5 mm and PPD greater 6 mm, and above 50% alveolar bone loss in at least two quadrants which was commensurate with the amount of plaque accumulation. They also had the mean BOP values above 63%. The G group (n = 17) had varying degrees of gingival inflammation with the mean BOP values above 50%, but no clinical attachment loss >2 mm, no sites with alveolar bone loss present in radiography (the distance between the cementoenamel junction and bone crest less 3 mm at above 95% of the proximal tooth sites). The individuals with periodontal health had no sites with PD greater 3 mm and CAL greater 2 mm, a mean BOP below 15% at the time of examination, and no detectable alveolar bone loss. The demographic and clinical details of details of the participants included in the analysis are presented in supplemental File S1. The whole saliva samples were obtained in the morning between 8.00 am–10.00 am, as this is the least variable time point during the day for saliva composition (31.Dawes C. Circadian rhythms in human salivary flow rate and composition.J. Physiol. 1972; 220: 529-545Crossref PubMed Scopus (358) Google Scholar). The unstimulated saliva samples were collected by expectorating into sterile 50 ml tubes for 5 min as described earlier (32.Gumus P. Emingil G. Ozturk V.O. Belibasakis G.N. Bostanci N. Oxidative stress markers in saliva and periodontal disease status: modulation during pregnancy and postpartum.BMC Infect. Dis. 2015; 15: 261Crossref PubMed Scopus (36) Google Scholar). Briefly, the participants were asked to avoid oral hygiene practices including flossing, brushing, and mouth-rinses as well as eating, and drinking for at least 2 h before collection. Before clinical periodontal measurements, each participant was asked first to rinse the mouth completely with water for 2 min, wait for 10 min, and then expectorate into sterile tubes for 5 min. On the day of analysis, the samples were thawed on ice and centrifuged at 10,000 × g for 15 min at 4 °C. The obtained supernatants were supplemented with the EDTA-free Protease Inhibitor Mixture (Sigma-Aldrich, Dorset, UK). Total protein content of the collected supernatants were measured with Qubit® Protein Assay Kit (Thermo Scientific, Wohlen, Switzerland). Despite that the saliva collection was standardized by time, there were considerable inter-individual variations in the total protein concentrations. The total protein concentrations for H, G, CP, and AP (μg/ml, median (min-max)) were 764.3 (522–1290), 1110 (637–1970), 1140 (693–1850), 1118 (694–1760), respectively. The median total protein levels were found to be significantly different among the groups (p < 0.01). Therefore, total protein amount per sample was controlled. Solutions of 80 μg of total protein per sample were subjected to in-solution trypsin digestion according to the RapiGest protocol. Briefly, the supernatants were diluted with ammonium bicarbonate buffer to reach a neutral pH, then RapiGest was added to the samples at the final concentration of 0.1%. Afterward, the samples were reduced with dithiothreitol by incubation at 60 °C for 30 min and carbamidomethylated using iodoacetamide at a final concentration of 15 mm for 30 min in dark. The samples were digested with trypsin in 0.05 m triethylammonium bicarbonate (1:100 w:w) overnight at 37 °C. Trifluoroacetic acid (TFA) was added to a final concentration of 0.5% and the samples were incubated for 30 min at 37 °C. Peptide mixtures were desalted using reverse phase cartridges Finisterre SPE C18 (Wicom International AG, Maienfeld, Switzerland) according to the manufacturer's specifications. Each sample was evaporated using a Speedvac (Thermo Scientific) and subsequentially reconstituted in 3% acetonitrile (ACN) and 0.1% formic acid (FA). Tryptic digests were analyzed on a LTQ Orbitrap Velos equipped with a nanospray ion source. Chromatographic separations of peptides on a Eksigent nanoLC-1D device (ABSciex, Concord, Ontario) coupled to an in-house pulled and packed tip column, 75 μm diameter, packed with Magic C18 AQ beads (3 μm bead size, 200 Å pore size) (Bishoff Chromatography, Leonberg, Germany). Peptides were loaded on the column from a cooled (4 °C) Eksigent autosampler and separated with a linear gradient of acetonitrile/water, containing 0.1% formic acid, at a flow rate of 200 nl/min. A gradient from 2 to 30% acetonitrile in 60 min was used. Mass spectra were acquired in a data-dependent manner, with an automatic switch between MS and MS/MS using a top 10 method. MS spectra were acquired in the Orbitrap analyzer with a mass range of 300–2000 m/z, with a resolution of 30,000 in the Orbitrap. Collision-induced dissociation (CID) peptide fragments were acquired in the ion trap with a collision energy of 35, activation energy of 0.25 and 30 ms activation time, excluding singly charged ions for fragmentation. Fragmented peptides were put on a dynamic exclusion list with a list size of 500 and an expiration time fo 90 s. The raw files from the mass spectrometer were uploaded onto the Progenesis LC-MS (version 4.1, Nonlinear Dynamics, Newcastle upon Tyne, UK). The LC-MS data were normalized and aligned according to the manufacturer's specifications. The Mascot generic file (.mgf file format) generated with Progenesis LC-MS (using up to five tandem mass spectra for each feature with the top 200 fragment ion peaks, charge deconvolution and deisotoping option applied) was searched against an in-house built database, constructed using human, bacterial and fungal species, including combination of common contaminants and reversed sequences (a total of 249,061 sequences) using the Mascot 2.4.1 search engine (Matrix Science, London, UK) in order to evaluate the false discovery rate (FDR) using the target-decoy strategy (http://fgcz-ms/FASTA/p963_db1_d_20111201.fasta). All sequences were downloaded from NCBI on May 27, 2016 and concatenated to 261-sequences known as MS contaminants and reversed (decoyed) to generate the search database. The selected parameters included precursor tolerance (15 ppm) and [ss2] fragment ion tolerance (0.6 Da). Trypsin was used as the protein-cleaving enzyme, and three missed cleavages were allowed. Variable modifications included oxidation of methionine, deamidation from glutamine and asparagine and N-terminal acetylation of proteins whereas carbamidomethylation of cysteine was selected as a fixed modification. The mascot result was loaded into Scaffold v4.1.1 using 95% PeptideProphet and ProteinProphet thresholds and protein cluster analysis. The spectrum report was exported and loaded into Progenesis LC-MS. The experimental design consisted of the following groups: H Versus G
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