Dataset Information

Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease.

ABSTRACT: 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.

SUBMITTER: Bostanci N

PROVIDER: S-EPMC6030726 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease.

Bostanci Nagihan N Selevsek Nathalie N Wolski Witold W Grossmann Jonas J Bao Kai K Wahlander Asa A Trachsel Christian C Schlapbach Ralph R Öztürk Veli Özgen VÖ Afacan Beral B Emingil Gulnur G Belibasakis Georgios N GN

Molecular & cellular proteomics : MCP 20180402 7

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 prot ...[more]

PMID: 29610270

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Project description:Phase 1: The whole saliva samples were obtained from 66 systemically healthy subjects consisting of patients with chronic periodontitis (CP), generalized aggressive periodontitis (G-AgP), gingivitis and individuals with periodontal health. The use of humans for study satisfied the requirements of the Ege University Institutional Review Board (Ethics number 16-12.1/16) and and was conducted in accordance with the guidelines of the World Medical Association Declaration of Helsinki. It is confirmed that this cross-sectional study conforms to STROBE guidelines for observational studies. Complete medical and dental histories were taken 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. Smoking status was also recorded. 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 to use their saliva samples and individual data for scientific purposes. The clinical periodontal indices including probing depth (PPD), clinical attachment level (CAL), plaque index (PI) and bleeding on probing (BOP) were recorded by a manual periodontal probe by a trained and calibrated examiner (V.O.O). The extent and severity of alveolar bone support were 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 (REF). Generalized Aggressive Periodontitis (G-AgP): The G-AgP 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. These individuals had minimum of CAL ≥ 5 mm and PD ≥ 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 ≥3 permanent teeth other than first molars and incisors. Generalized Chronic Periodontitis (CP): The CP group included 17 patients with severe generalized chronic periodontitis. These individuals had a minimum four non-adjacent teeth with sites with CAL ≥ 5 mm and PPD ≥ 6 mm, and ≥50 % alveolar bone loss in at least two quadrants which was commensurate with the amount of plaque accumulation. Phase 2: We aimed to develop SRM-based workflow for detecting and quantitating the relative abundance of the candidate proteins in an independent saliva cohort . 82 subjects (48 females and 34 males; age range 24-59 years) were recruited for the study at the Department of Periodontology, School of Dentistry, Adnan Menderes University, Aydın, Turkey. Ethical clearance was obtained from the Ethics Committee of the School of Medicine, Ege University with the protocol number ((Ethics number 70198063-050.06.04). Each participant gave written and verbal informed consent after the purpose and procedures of the study were explained. A dental and medical history was compiled for all subjects as detailed in phase 1 and participants were clustered into four groups according to the criteria proposed by the 1999 International Workshop for a Classification of Periodontal Diseases and Conditions as detailed at Phase 1

Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Dataset Information

Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease.

Publications

Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease.

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