Novel protein identification methods for biomarker discovery via a proteomic analysis of periodontally healthy and diseased gingival crevicular fluid samples

Aim To identify possible novel biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals using high‐throughput proteomic analysis. Materials and Methods Gingival crevicular fluid samples were collected from 12 CP and 12 periodontally...

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Published inJournal of clinical periodontology Vol. 39; no. 3; pp. 203 - 212
Main Authors Baliban, Richard C., Sakellari, Dimitra, Li, Zukui, DiMaggio, Peter A., Garcia, Benjamin A., Floudas, Christodoulos A.
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.03.2012
Blackwell
Subjects
Bacterial proteins
Bacterial Proteins - analysis
Biological and medical sciences
Biomarkers
Biomarkers - analysis
Carbonic Anhydrases - analysis
Case-Control Studies
Chromatography
Chronic Periodontitis - metabolism
Defensins - analysis
Facial bones, jaws, teeth, parodontium: diseases, semeiology
gingival crevicular fluid
Gingival Crevicular Fluid - chemistry
Humans
Medical sciences
Methods
Non tumoral diseases
Otorhinolaryngology. Stomatology
Peptide Elongation Factor 1 - analysis
periodontitis
Proteome - analysis
proteomic analysis
Proteomics - methods
RNA polymerase
Software
Tandem Mass Spectrometry
Stomatology
Crevicular fluid
biomarkers
Biological marker
Identification
Dentistry
Protein
proteomic analysis
Periodontal disease
tandem mass spectrometry
Periodontitis
Mass spectrometry MS/MS
gingival crevicular fluid
Gingival fluid
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Summary:Aim To identify possible novel biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals using high‐throughput proteomic analysis. Materials and Methods Gingival crevicular fluid samples were collected from 12 CP and 12 periodontally healthy subjects. Samples were trypically digested with trypsin, eluted using high‐performance liquid chromatography, and fragmented using tandem mass spectrometry (MS/MS). MS/MS spectra were analysed using PILOT_PROTEIN to identify all unmodified proteins within the samples. Results Using the database derived from Homo sapiens taxonomy and all bacterial taxonomies, 432 human (120 new) and 30 bacterial proteins were identified. The human proteins, angiotensinogen, clusterin and thymidine phosphorylase were identified as biomarker candidates based on their high‐scoring only in samples from periodontal health. Similarly, neutrophil defensin‐1, carbonic anhydrase‐1 and elongation factor‐1 gamma were associated with CP. Candidate bacterial biomarkers include 33 kDa chaperonin, iron uptake protein A2 and phosphoenolpyruvate carboxylase (health‐associated) and ribulose biphosphate carboxylase, a probable succinyl‐CoA:3‐ketoacid‐coenzyme A transferase, or DNA‐directed RNA polymerase subunit beta (CP‐associated). Most of these human and bacterial proteins have not been previously evaluated as biomarkers of periodontal conditions and require further investigation. Conclusions The proposed methods for large‐scale comprehensive proteomic analysis may lead to the identification of novel biomarkers of periodontal health or disease.
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Table S1. Human protein list for the twelve healthy (H) and twelve diseased (D) samples. The database is derived from the swissprot database and uses a combination of homo sapiens and all bacterial taxonomies identified to be associated with periodontal health. For each protein, the total number (No.) of samples containing that protein is listed along with the average number of assigned peptides (Pep.) and the average number of assigned spectra (Spec.).Table S2. Human protein list having a minimum of two identified peptides for the twelve healthy (H) and twelve diseased (D) samples. The database is derived from the swissprot database and uses a combination of homo sapiens and all bacterial taxonomies identified to be associated with periodontal health. For each protein, the total number (No.) of samples containing that protein is listed along with the average number of assigned peptides (Pep.) and the average number of assigned spectra (Spec.). A protein identification for a given LC-MS/MS scan is considered valid only if at least two identified peptides are assigned to the protein.Table S3. Human healthy protein gene ontology. The total number of proteins that correspond to the specific ontological feature is reported.Table S4. Human diseased protein gene ontology. The total number of proteins that correspond to the specific ontological feature is reported.
C.A.F. and B.A.G. acknowledge financial support from the National Science Foundation (CBET‐0941143). C.A.F. acknowledges financial support from the National Institute of Health (R01LM009338). Although the research described in the article has been funded in part by the U.S. Environmental Protection Agency's STAR program through grant (R 832721‐010), it has not been subjected to any EPA review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred. B.A.G. acknowledges support from Princeton University and the American Society for Mass Spectrometry Research award. The authors declare that there are no conflicts of interest in this study.
Conflict of interest and source of funding statement
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ISSN:0303-6979
1600-051X
1600-051X
DOI:10.1111/j.1600-051X.2011.01805.x