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.
Highlights
Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases
General Overview of Discovery Stage, Non-targeted Quantitative Proteomic Findings—In analyzing the collected saliva samples, the first approach was to use a non-targeted shotgun proteomics combined with a label-free quantitative approach whereby comparative precursor-ion pattern is used, based on the direct comparison of mass spectrometry (MS) peptide signals [20, 38] (Fig. 1)
After exclusion of reverse sequences, the identified bacterial proteins originated from Aggregatibacter actinomycetemcomitans (n ϭ 3), Campylobacter rectus (n ϭ 1), Fusobacterium nucleatum (n ϭ 2), Porphyromonas gingivalis (n ϭ 2), Prevotella oralis (n ϭ 1), Streptococcus anginosus (n ϭ 1), Treponema denticola (n ϭ 1), Veillonella dispar (n ϭ 2), Streptococcus oralis (n ϭ 2)
Summary
Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. 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. The use of quantitative proteomics for characterizing periodontal diseases offers significant potential for providing “periodontal disease related finger prints” [10, 11] In this respect, saliva is a useful reservoir of clinically relevant biomarkers for reflection of peri-. A better approach is to aim for a panel of related markers for conclusive prediction
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