Progression from healthy periodontium to gingivitis and periodontitis: Insights from bioinformatics-driven proteomics - A systematic review with meta-analysis

AIM

The current study aimed to: (1) systematically review the published literature regarding the proteomics analyses of saliva and gingival crevicular fluid (GCF) in healthy humans and gingivitis and/or periodontitis patients; and (2) to identify the differentially expressed proteins (DEPs) based on the systematic review, and comprehensively conduct meta-analyses and bioinformatics analyses.

METHODS

An online search of Web of Science, Scopus, and PubMed was performed without any restriction on the year and language of publication. After the identification of the DEPs reported by the included human primary studies, gene ontology (GO), the Kyoto encyclopedia of genes and genomes pathway (KEGG), protein-protein interaction (PPI), and meta-analyses were conducted. The risk of bias among the included studies was evaluated using the modified Newcastle-Ottawa quality assessment scale.

RESULTS

The review identified significant differences in protein expression between healthy individuals and those with gingivitis and periodontitis. In GCF, 247 proteins were upregulated and 128 downregulated in periodontal diseases. Saliva analysis revealed 79 upregulated and 70 downregulated proteins. There were distinct protein profiles between gingivitis and periodontitis, with 159 and 31 unique upregulated proteins in GCF, respectively. Meta-analyses confirmed significant upregulation of various proteins in periodontitis, including ALB and MMP9, while CSTB and GSTP1 were downregulated. AMY1A and SERPINA1 were upregulated in periodontitis saliva. HBD was upregulated in gingivitis GCF, while DEFA3 was downregulated. PPI analysis revealed complex networks of interactions among DEPs. GO and KEGG pathway analyses provided insights into biological processes and pathways associated with periodontal diseases.

CONCLUSION

The ongoing MS-based proteomics studies emphasize the need for a highly sensitive and specific diagnostic tool for periodontal diseases. Clinician acceptance of the eventual diagnostic method relies on its ability to provide superior or complementary information to current clinical assessment procedures. Future research should prioritize the multiplex measurement of multiple biomarkers simultaneously to enhance diagnostic accuracy and large study cohorts are necessary to ensure the validity and reliability of research findings.

Proteomics for the discovery of biomarkers and diagnosis of periodontitis: a critical review

Periodontitis is a common chronic and destructive disease whose pathogenetic mechanisms remain unclear. Due to their sensitivity and global scale, proteomics studies offer the opportunity to uncover critical host and pathogen activity indicators and can elucidate clinically applicable biomarkers for improved diagnosis and treatment of the disease. This review summarizes the literature of proteomics studies on periodontitis and comprehensively discusses commonly found candidate biomarkers. Key considerations in the design of an experimental proteomics platform are also outlined. The applicability of protein biomarkers across the progression of periodontitis and unexplored areas of research are highlighted.

Chromosome-centric approach to overcoming bottlenecks in the Human Proteome Project

The international Human Proteome Project (HPP), a logical continuation of the Human Genome Project, was launched on 23 September 2010 in Sydney, Australia. In accordance with the gene-centric approach, the goals of the HPP are to prepare an inventory of all human proteins and decipher the network of cellular protein interactions. The greater complexity of the proteome in comparison to the genome gives rise to three bottlenecks in the implementation of the HPP. The main bottleneck is the insufficient sensitivity of proteomic technologies, hampering the detection of proteins with low- and ultra-low copy numbers. The second bottleneck is related to poor reproducibility of proteomic methods and the lack of a so-called 'gold' standard. The last bottleneck is the dynamic nature of the proteome: its instability over time. The authors here discuss approaches to overcome these bottlenecks in order to improve the success of the HPP.

[Variability of healthy human proteome]

The purpose of this review is to analyze investigations devoted to characteristic of protein variability and diversity of their posttranslational modifications in healthy humans. The numerous researches have demonstrated that proteomic profile has a considerable both intra- and inter-individual variability, and quite often normal variability of some proteins can be comparable to changes observed in pathological processes. Results obtained by our research group have confirmed high intra-individual variability of serum low-molecular subproteome of healthy volunteers, certified by a special medial committee. Proteins characterized by high variability in normal conditions (e.g. haptoglobin--0-40 mg/ml; lysozyme--0,01-0,1 mg/ml; C-reactive protein--0,01-0,3 mg/ml) should be excluded from the list of potential biomarkers. On the contrary, proteins and peptides characterized by insignificant dispersion in healthy population (such as albumin--coefficient of variation (CV) 9%; transferrin--CV14%; C3c complement--CV 17%, alpha-1 acid glycoprotein--CV 21%, alpha2-macroglobulin--CV 20%; transthyretin fragment--CV 28,3% and beta-chain alpha2-HS-glycoprotein--CV 29,7%) can provide us with important information about state of health. Thus investigations of plasticity in proteomic profiles of healthy humans will help to correct reference intervals used in clinical proteomics.

[Selection of the peptide mass tolerance value for the protein identification with peptide mass fingerprinting]

Peptide mass-fingerprint is widely used for protein identification while studying proteome with the use of 1D or 2D electrophoresis. Peptide mass tolerance indicates the fit of theoretical peptide mass with the experimental measurements, and choice of this parameter sufficiently influences the protein identification. The role of peptide mass tolerance was estimated by counting the number of identified proteins for the reference set of mass-spectra. The reference set of 400 Ultraflex (Bruker Daltonics, Germany) mass-spectra was obtained for the slices of 1D gel of liver microsomes. Using Mascot server for protein identification, the peptide mass tolerance value was varied in the range from 0.02 to 0.40 Da with a step 0.01 Da. Depending on the tolerance the number of identified protein changes up to 10 times. Maximal number of identified proteins was reported for the tolerance value of 0.15 Da (120 ppm), which is 1.5 - 2 times higher than the recommended values for such type of mass-spectrometers. The software program PMFScan was developed to obtain the dependence of number of identified proteins of the tolerance values.