Identification of biomarkers for periodontal disease using the immunoproteomics approach

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.

Identification and Validation of Signature Genes and Potential Therapy Targets of Inflammatory Bowel Disease and Periodontitis

Background

Inflammatory bowel disease (IBD) and periodontitis (PD) are correlated, although the pathogenic mechanism behind their correlation has not been clarified. This study aims to explore the common signature genes and potential therapeutic targets of IBD and PD using transcriptomic analysis.

Methods

The GEO database was used to download datasets of IBD and PD, and differential expression analysis was used to identify DEGs. We then conducted GO and KEGG enrichment analyses of the shared genes. Next, we applied 4 machine learning (ML) algorithms (GLM, RF, GBM, and SVM) to select the best prediction model for diagnosing the disease and obtained the hub genes of IBD and PD. The diagnostic value of the signature genes was verified by a validation set and qRT‒PCR experiments. Subsequently, immune cell infiltration in IBD samples and PD samples was analyzed by ssGSEA. Finally, we investigated and validated the response of hub genes to infliximab therapy.

Results

We identified 43 upregulated genes as shared genes by intersecting the DEGs of IBD and PD. Functional enrichment analysis suggested that the shared genes were closely associated with immunity and inflammation. The ML algorithm and qRT‒PCR results indicated that IGKC and COL4A1 were the hub genes with the most diagnostic value for IBD and PD. Subsequently, through immune infiltration analysis, CD4 T cells, NK cells and neutrophils were identified to play crucial roles in the pathogenesis of IBD and PD. Finally, through in vivo and in vitro experiments, we found that IGKC and COL4A1 were significantly downregulated during the treatment of patients with IBD using infliximab.

Conclusion

We investigated the potential association between IBD and PD using transcriptomic analysis. The IGKC and COL4A1 genes were identified as characteristic genes and novel intervention targets for these two diseases. Infliximab may be used to treat or prevent IBD and PD.

Next-Generation Examination, Diagnosis, and Personalized Medicine in Periodontal Disease

Periodontal disease, a major cause of tooth loss, is an infectious disease caused by bacteria with the additional aspect of being a noncommunicable disease closely related to lifestyle. Tissue destruction based on chronic inflammation is influenced by host and environmental factors. The treatment of periodontal disease varies according to the condition of each individual patient. Although guidelines provide standardized treatment, optimization is difficult because of the wide range of treatment options and variations in the ideas and skills of the treating practitioner. The new medical concepts of “precision medicine” and “personalized medicine” can provide more predictive treatment than conventional methods by stratifying patients in detail and prescribing treatment methods accordingly. This requires a new diagnostic system that integrates information on individual patient backgrounds (biomarkers, genetics, environment, and lifestyle) with conventional medical examination information. Currently, various biomarkers and other new examination indices are being investigated, and studies on periodontal disease-related genes and the complexity of oral bacteria are underway. This review discusses the possibilities and future challenges of precision periodontics and describes the new generation of laboratory methods and advanced periodontal disease treatment approaches as the basis for this new field.

Biobanking in dentistry: A review

Biobanks are not-for-profit services for the collection, processing, storage and distribution of biological samples and data for research and diagnostic purposes. In dentistry, biological materials and data obtained from questionnaires investigating oral conditions can be stored and used for large-scale studies on oral and systemic diseases. To give some examples: gene expression microarrays obtained on biobanked specimens were used in the identification of genetic alterations in oral cancer; efforts to identify genetic mechanisms behind dental caries have been based on an integrative analysis of transcriptome-wide associations and messenger RNA expression. One of the largest studies on facial pain was conducted using Biobank data. Cryopreservation of dental pulp stem cells is a common practice in tooth biobanks. With the exception of teeth and pulp, also leftover oral soft and hard tissues may represent a source of healthy samples that has rarely been exploited as yet. While biobanks are increasingly attracting the attention of the scientific community and becoming economically sustainable, a systematic approach to this resource in dentistry seems to be lacking. This review illustrates the applications of biobanking in dentistry, describing biobanked pathological and healthy samples and data, and discussing future developments.

Peptidomic changes of saliva after nonsurgical treatment of stage I / II generalized periodontitis

OBJECTIVE

To explore the changes of peptidome profiles of saliva, serum and gingival crevicular fluid (GCF) before and after nonsurgical periodontal treatment in patients with generalized periodontitis (stage I / II).

SUBJECTS AND METHODS

Saliva, serum and GCF samples were collected from 17 patients at baseline (T₀ ), one week after ultrasonic supragingival scaling (T₁ ) and eight weeks after subgingival scaling and root planning (T₂ ). Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) was carried out to detect changes in peptidomic profiles. Then nano-liquid chromatography-electrospray ionization-tandem mass spectrometry (nano-LC/ESI-MS/MS) was performed to identify potential peptide biomarkers.

RESULTS

Most of the peptides from the patients exhibited a decreasing trend from the time point of pre-treatment to that of post-treatment. Cluster analysis and scatter plots using these peptides indicated that salivary peptidome has an acceptable capability of reflecting the status of stage I / II generalized periodontitis. Seven of these peptides were successfully identified as α-1-antitrypsin, immunoglobulin κ variable 4-1, haptoglobin and immunoglobulin heavy constant γ2.

CONCLUSIONS

Certain peptides in saliva, serum and GCF were down-regulated after nonsurgical periodontal treatment, demonstrating the application prospects of saliva in monitoring and surveillance of periodontal diseases in both clinical settings and communities.

Finding distinctions between oral cancer and periodontitis using saliva metabolites and machine learning

OBJECTIVE

The aim of this research is the study of metabolic pathways related to oral cancer and periodontitis along with development of machine-learning model for elucidation of these diseases based on saliva metabolites of patients.

METHODS

Data mining, metabolomic pathways analysis, study of metabolite-gene networks related to these diseases. Machine-learning and deep-learning methods for development of the model for recognition of oral cancer versus periodontitis, using patients' saliva.

RESULTS

The most accurate classifications between oral cancer and periodontitis were performed using neural networks, logistic regression and stochastic gradient descent confirmed by the separate 10-fold cross-validations. The best results were achieved by the deep-learning neural network with the TensorFlow program. Accuracy of the resulting model was 79.54%. The other methods, which did not rely on deep learning, were able to achieve comparable, although slightly worse results with respect to accuracy.

CONCLUSION

Our results demonstrate a possibility to distinguish oral cancer from periodontal disease by analysis the saliva metabolites of a patient, using machine-learning methods. These findings may be useful in the development of a non-invasive method to aid care providers in determining between oral cancer and periodontitis quickly and effectively.

Proteomics approach for biomarkers and diagnosis of periodontitis: systematic review

Quantitative proteomic workflow based on mass spectrometry (MS) is recently developed by the researchers to screen for biomarkers in periodontal diseases comprising periodontitis. Periodontitis is known for chronic inflammatory disease characterized by progressive destruction of the tooth-supporting apparatus, yet has a lack of clear pathobiology based on a discrepancy between specified categories and diagnostic vagueness. The objective of this review was to outlined the accessible information related to proteomics studies on periodontitis. The Preferred Reporting Items for Systematical Reviews and Meta-Analysis (PRISMA) statement guides to acquaint proteomic analysis on periodontal diseases was applied. Three databases were used in this study, such as Pubmed, ScienceDirect and Biomed Central from 2009 up to November 2019. Proteomics analysis platforms that used in the studies were outlined. Upregulated and downregulated proteins findings data were found, in which could be suitable as candidate biomarkers for this disease.

Identification of New Peptide Biomarkers for Bacterial Bloodstream Infection

PURPOSE

Due to a lack of effective early diagnostic measures, new diagnostic methods for bacterial bloodstream infections (BSIs) are urgently needed. A protein-peptide profiling approach can be used to identify novel diagnostic biomarkers of BSIs.

EXPERIMENTAL DESIGN

In this study, MALDI-TOF MS and nano-LC/ESI-MS/MS are used to analyze serum peptides. In addition, GO and network analyses are conducted as a means of analyzing these potential protein markers. Finally, the potential biomarkers are verified in independent clinical samples via ELISA.

RESULTS

m/z 1533.8, 2794.3, 3597.3, 5007.3, and 7816.7 reveal an identical trend; the intensity of m/z 1533.8, 2794.3, and 3597.3 are higher in the infection group relative to controls, whereas the intensity of m/z 5007.3 and 7816.7 are lower in the infection group. Four peaks are successfully identified including ITIH4, KNG1, SAA2, and C3. GO and network analyses find these proteins to form an interaction network, which may be correlated with BSI. ELISA results indicate that ITIH4, KNG1, and SAA2 are effective in differentiating infected from normal control group and the febrile group.

CONCLUSIONS AND CLINICAL RELEVANCE

These biomarkers have the potential to offer new insights into the signaling networks underlying the development and progression of BSI.

Assessing a multiplex-targeted proteomics approach for the clinical diagnosis of periodontitis using saliva samples

AIM

The present study focused on the research of new biomarkers based on the liquid chromatography-multiple-reaction monitoring (MRM) proteomic profile in whole saliva of patients with periodontitis compared with periodontal healthy patients.

METHODS

A 30-min multiplexed liquid chromatography-MRM method was used for absolute quantification of 35 plasma biomarkers in saliva from control patients and patients with periodontitis.

RESULTS

Three proteins namely hemopexin, plasminogen and α-fibrinogen were shown to be clearly related to the presence of periodontitis compared with healthy patients. Apolipoprotein H was found to discriminate for the first time chronic and aggressive periodontitis.

CONCLUSION

Our results indicate that this innovative MRM method could be used to screen for periodontitis in clinical environment. Furthermore, apolipoprotein H was found to be a discriminant biomarker of aggressive periodontitis.

Levels of serum sclerostin, metabolic parameters, and periodontitis in -postmenopausal women with diabetes

Diabetes mellitus (DM) is a metabolic disease defined by hyperglycemia, which is associated with periodontal disease and exerts an effect on bone metabolism. The aim of this study was to determine serum levels of sclerostin in postmenopausal women with diabetes and determine a possible association with periodontal disease. Sixty-one postmenopausal women (32 with diabetes and 29 without diabetes) were evaluated. Blood was collected for biochemical analysis and the determination of serum sclerostin. The participants were also submitted to a clinical examination for the evaluation of periodontal status. A total of 75.4% of the volunteers had periodontal disease and levels serum sclerostin were altered in 48.7% of the patients with diabetes. In the diabetic population, mean levels of LDL (p = 0.035) and urea (p = 0.032) were higher in the patients without periodontal disease and the plaque index was higher in those with periodontal disease (p = 0.039). The prevalence of periodontal disease and the levels serum sclerostin were high in the postmenopausal women analyzed, but the data do not allow the determination of whether periodontal disease is related to high levels of this peptide.