A Systems Biology Approach to Reveal Putative Host-Derived Biomarkers of Periodontitis by Network Topology Characterization of MMP-REDOX/NO and Apoptosis Integrated Pathways

Association of rs142548867 (EEFSEC) and periodontitis Grade C in a young Brazilian population

BACKGROUND

Periodontitis Stage III-IV, Grade C (PerioC) is a severe form of Periodontitis. The individual genetic background has been shown to be an important etiopathogenic factor for the development of this disease in young, systemically healthy, and non-smokers patients. Recently, after exome sequencing of families with a history of the disease, PerioC was associated with three single nucleotide variations (SNVs) - rs142548867 (EEFSEC), rs574301770 (ZNF136), and rs72821893 (KRT25) - which were classified as deleterious or possibly harmful by prediction algorithms.

OBJECTIVE

Seeking to validate these findings in a cohort evaluation, this study aims to characterize the allele and genotypic frequency of the SNVs rs142548867, rs574301770, and rs72821893 in the Brazilian population with PerioC and who were periodontally healthy (PH).

METHODOLOGY

Thus, epithelial oral cells from 200 PerioC and 196 PH patients were harvested at three distinct centers at the Brazilian Southern region, their DNA were extracted, and the SNVs rs142548867, rs574301770, rs72821893 were genotyped using 5'-nuclease allelic discrimination assay. Differences in allele and genotype frequencies were analyzed using Fisher's Exact Test. Only the SNV rs142548867 (C > T) was associated with PerioC.

RESULTS

The CT genotype was detected more frequently in patients with PerioC when compared with PH subjects (6% and 0.5% respectively), being significantly associated with PerioC (odds ratio 11.76, p=0.02).

CONCLUSION

rs142548867 represents a potential risk for the occurrence of this disease in the Brazilian population.

Systems Biology in Periodontitis

Systems biology is a promising scientific discipline that allows an integrated investigation of host factors, microbial composition, biomarkers, immune response and inflammatory mediators in many conditions such as chronic diseases, cancer, neurological disorders, and periodontitis. This concept utilizes genetic decoding, bioinformatic, flux-balance analysis in a comprehensive approach. The aim of this review is to better understand the current literature on systems biology and identify a clear applicability of it to periodontitis. We will mostly focus on the association between this condition and topics such as genomics, transcriptomics, proteomics, metabolomics, as well as contextualize delivery systems for periodontitis treatment, biomarker detection in oral fluids and associated systemic conditions.

Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO)

BACKGROUND

Chronic periodontitis (CP) is a multifactorial inflammatory disease. For the diagnosis of CP, it is necessary to investigate molecular biomarkers and the biological pathway of CP. Although analysis of mRNA expression profiling with microarray is useful to elucidate pathological mechanisms of multifactorial diseases, it is expensive. Therefore, we utilized pooled microarray gene expression data on the basis of data sharing to reduce hybridization costs and compensate for insufficient mRNA sampling. The aim of the present study was to identify molecular biomarker candidates and biological pathways of CP using pooled datasets in the Gene Expression Omnibus (GEO) database.

METHODS

Three pooled transcriptomic datasets (GSE10334, GSE16134, and GSE23586) of gingival tissue with CP in the GEO database were analyzed for differentially expressed genes (DEGs) using GEO2R, functional analysis and biological pathways with the Database of Annotation Visualization and Integrated Discovery database, Protein-Protein Interaction (PPI) network and hub gene with the Search Tool for the Retrieval of Interaction Genes database, and biomarker candidates for diagnosis and prognosis and upstream regulators of dominant biomarker candidates with the Ingenuity Pathway Analysis database.

RESULTS

We shared pooled microarray datasets in the GEO database. One hundred and twenty-three common DEGs were found in gingival tissue with CP, including 81 upregulated genes and 42 downregulated genes. Upregulated genes in Gene Ontology were significantly enriched in immune responses, and those in the Kyoto Encyclopedia of Genes and Genomes pathway were significantly enriched in the cytokine-cytokine receptor interaction pathway, cell adhesion molecules, and hematopoietic cell lineage. From the PPI network, the 12 nodes with the highest degree were screened as hub genes. Additionally, six biomarker candidates for CP diagnosis and prognosis were screened.

CONCLUSIONS

We identified several potential biomarkers for CP diagnosis and prognosis (e.g., CSF3, CXCL12, IL1B, MS4A1, PECAM1, and TAGLN) and upstream regulators of biomarker candidates for CP diagnosis (TNF and TGF2). We also confirmed key genes of CP pathogenesis such as CD19, IL8, CD79A, FCGR3B, SELL, CSF3, IL1B, FCGR2B, CXCL12, C3, CD53, and IL10RA. To our knowledge, this is the first report to reveal associations of CD53, CD79A, MS4A1, PECAM1, and TAGLN with CP.

Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.

Cumulative use of salivary markers with an adaptive design improves detection of periodontal disease over fixed biomarker thresholds

OBJECTIVE

Aim was to analyze the diagnostic ability of cumulative risk score (CRS), which uses salivary levels of Porphyromonas gingivalis, interleukin (IL)-1β, and matrix metalloproteinase (MMP)-8 in an adaptive design, compared to previously reported thresholds of each marker alone.

MATERIALS AND METHODS

Oral and general health information of 463 participants were included in the analysis. Having the percentage of bleeding on probing (BOP) > 25%, having at least two sites with probing pocket depth (PPD) of 4-5 mm or having at least one tooth with alveolar bone loss (ABL) of at least 1/3 of the root length were accepted as outcome variables. Being above the salivary threshold concentrations of P. gingivalis, IL-1β, and MMP-8 and CRS values were used as explanatory variables. Receiver operating characteristics (ROC) producing an area under the curve (AUC) and multinomial regression analysis were used in statistical analysis.

RESULTS

CRS provided AUCs larger than any other tested biomarker threshold. Sensitivity and specificity of CRS for detecting clinical markers of periodontitis were acceptable, and a strong association was observed between the highest CRS score and having at least two sites with PPD of 4-5 mm.

CONCLUSION

CRS brings additional power over fixed thresholds of single biomarkers in detecting periodontitis.

Salivary biomarkers in association with periodontal parameters and the periodontitis risk haplotype

Genetic factors play a role in periodontitis. Here we examined whether the risk haplotype of MHC class III region BAT1-NFKBIL1-LTA and lymphotoxin-α polymorphisms associate with salivary biomarkers of periodontal disease. A total of 455 individuals with detailed clinical and radiographic periodontal health data were included in the study. A 610 K genotyping chip and a Sequenom platform were used in genotyping analyses. Phospholipid transfer protein activity, concentrations of lymphotoxin-α, IL-8 and myeloperoxidase, and a cumulative risk score (combining Porphyromonas gingivalis, IL-1β and matrix metalloproteinase-8) were examined in saliva samples. Elevated IL-8 and myeloperoxidase concentrations and cumulative risk scores associated with advanced tooth loss, deepened periodontal pockets and signs of periodontal inflammation. In multiple logistic regression models adjusted for periodontal parameters and risk factors, myeloperoxidase concentration (odds ratio (OR); 1.37, P = 0.007) associated with increased odds for having the risk haplotype and lymphotoxin-α concentration with its genetic variants rs2857708, rs2009658 and rs2844482. In conclusion, salivary levels of IL-8, myeloperoxidase and cumulative risk scores associate with periodontal inflammation and tissue destruction, while those of myeloperoxidase and lymphotoxin-α associate with genetic factors as well.

Dual action of bacteriocin PLNC8 αβ through inhibition of Porphyromonas gingivalis infection and promotion of cell proliferation

Periodontitis is a chronic inflammatory disease that is characterised by accumulation of pathogenic bacteria, including Porphyromonas gingivalis, in periodontal pockets. The lack of effective treatments has emphasised in an intense search for alternative methods to prevent bacterial colonisation and disease progression. Bacteriocins are bacterially produced antimicrobial peptides gaining increased consideration as alternatives to traditional antibiotics. We show rapid permeabilisation and aggregation of P. gingivalis by the two-peptide bacteriocin PLNC8 αβ. In a cell culture model, P. gingivalis was cytotoxic against gingival fibroblasts. The proteome profile of fibroblasts is severely affected by P. gingivalis, including induction of the ubiquitin-proteasome pathway. PLNC8 αβ enhanced the expression of growth factors and promoted cell proliferation, and suppressed proteins associated with apoptosis. PLNC8 αβ efficiently counteracted P. gingivalis-mediated cytotoxicity, increased expression of a large number of proteins and restored the levels of inflammatory mediators. In conclusion, we show that bacteriocin PLNC8 αβ displays dual effects by acting as a potent antimicrobial agent killing P. gingivalis and as a stimulatory factor promoting cell proliferation. We suggest preventive and therapeutical applications of PLNC8 αβ in periodontitis to supplement the host immune defence against P. gingivalis infection and support wound healing processes.

Network-Based Identification of Altered Stem Cell Pluripotency and Calcium Signaling Pathways in Metastatic Melanoma

Malignancy of cancer has been linked to distinct subsets of stem-like cells, the so-called cancer stem cells (CSCs), which persist during treatment and seem to lead to drug-resistant recurrence. Metastatic spread of cancer cells is one of the hallmarks of malignancy and contributes to most human melanoma-related deaths. Recently, overlapping groups of proteins and pathways were shown to regulate stem cell migration and cancer metastasis, raising the question of whether genes/proteins involved in stem cell pluripotency may have important implications when applied to the biology of cancer metastasis. Furthermore, it is well known that ion channels and receptors, particularly those responsible for calcium (Ca²⁺) signal generation, are critical in determining the cellular fate of stem cells (SCs). In the present study, we searched for evidence of altered stem cell pluripotency and Ca²⁺ signaling-related genes in the context of melanoma metastasis. We did this by using network analysis of gene expression in tissue biopsies from three different independent datasets of patients. First, we created an in silico network model (“STEMCa” interactome) showing the landscape of interactions between stem cell pluripotency and Ca²⁺ signaling-related genes/proteins, and demonstrated that around 51% (151 out of 294) of the genes within this model displayed significant changes of expression (False Discovery Rate (FDR), corrected p-value < 0.05) in at least one of the datasets of melanoma metastasis when compared with primary tumor biopsies (controls). Analysis of the properties (degree and betweenness) of the topological network revealed 27 members as the most central hub (HB) and nonhub-bottlenecks (NH-B) among the 294 genes/proteins of the whole interactome. From those representative genes, CTNNB1, GNAQ, GSK3B, GSTP1, MAPK3, PPP1CC, PRKACA, and SMAD4 showed equal up- or downregulation (corrected p-value < 0.05) in at least 2 independent datasets of melanoma metastases samples and PTPN11 showed upregulation (corrected p-value < 0.05) in three of them when compared with control samples. We postulate that altered expression of stem cell pluripotency and Ca²⁺ signaling pathway-related genes may contribute to the metastatic transformation, with these central members being an optimal candidate group of biomarkers and in silico therapeutic targets for melanoma metastasis, which deserve further investigation.

Ubiquitination in Periodontal Disease: A Review

Periodontal disease (periodontitis) is a chronic inflammatory condition initiated by microbial infection that leads to gingival tissue destruction and alveolar bone resorption. The periodontal tissue's response to dental plaque is characterized by the accumulation of polymorphonuclear leukocytes, macrophages, and lymphocytes, all of which release inflammatory mediators and cytokines to orchestrate the immunopathogenesis of periodontal disease. Ubiquitination is achieved by a mechanism that involves a number of factors, including an ubiquitin-activating enzyme, ubiquitin-conjugating enzyme, and ubiquitin-protein ligase. Ubiquitination is a post-translational modification restricted to eukaryotes that are involved in essential host processes. The ubiquitin system has been implicated in the immune response, development, and programmed cell death. Increasing numbers of recent reports have provided evidence that many approaches are delivering promising reports for discovering the relationship between ubiquitination and periodontal disease. The scope of this review was to investigate recent progress in the discovery of ubiquitinated protein in diseased periodontium and to discuss the ubiquitination process in periodontal diseases.

Using Coexpression Protein Interaction Network Analysis to Identify Mechanisms of Danshensu Affecting Patients with Coronary Heart Disease

Salvia miltiorrhiza, known as Danshen, has attracted worldwide interest for its substantial effects on coronary heart disease (CHD). Danshensu (DSS) is one of the main active ingredients of Danshen on CHD. Although it has been proven to have a good clinical effect on CHD, the action mechanisms remain elusive. In the current study, a coexpression network-based approach was used to illustrate the beneficial properties of DSS in the context of CHD. By integrating the gene expression profile data and protein-protein interactions (PPIs) data, two coexpression protein interaction networks (CePIN) in a CHD state (CHD CePIN) and a non-CHD state (non-CHD CePIN) were generated. Then, shared nodes and unique nodes in CHD CePIN were attained by conducting a comparison between CHD CePIN and non-CHD CePIN. By calculating the topological parameters of each shared node and unique node in the networks, and comparing the differentially expressed genes, target proteins involved in disease regulation were attained. Then, Gene Ontology (GO) enrichment was utilized to identify biological processes associated to target proteins. Consequently, it turned out that the treatment of CHD with DSS may be partly attributed to the regulation of immunization and blood circulation. Also, it indicated that sodium/hydrogen exchanger 3 (SLC9A3), Prostaglandin G/H synthase 2 (PTGS2), Oxidized low-density lipoprotein receptor 1 (OLR1), and fibrinogen gamma chain (FGG) may be potential therapeutic targets for CHD. In summary, this study provided a novel coexpression protein interaction network approach to provide an explanation of the mechanisms of DSS on CHD and identify key proteins which maybe the potential therapeutic targets for CHD.

Cancer/testis antigens as molecular drug targets using network pharmacology

Present chemotherapeutic drugs have limited efficacy and severe side effects. Considering the complexity of cancer, an effective strategy is necessary to discover multiple new drug targets. Cancer/testis antigens are vital for cancer cell progression. We have performed a computational network analysis of cancer/testis antigens and assessed these antigens as drug targets. During this analysis, protein interaction network of 700 human CT antigens was investigated. CT antigen network consisted of eight independent components. Four major hubs and two minor hubs were identified that play nodal role in the flow of information across the largest network. We have predicted 30 potential drug targets by analysing several topological parameters such as betweenness centrality, cluster coefficient and probable protein complexes. Structural and functional roles of potential drug targets have also been anatomized. Analysis of the CT antigen network enables us to pinpoint a set of candidate proteins that if targeted could be detrimental for cancerous cell without affecting any normal cell. The list of putative proteins is a starting point for experimental validation and may help further in the discovery of new anticancer drug targets.