Meta-Analysis of Two Human RNA-seq Datasets to Determine Periodontitis Diagnostic Biomarkers and Drug Target Candidates

Abstract

Periodontitis is a chronic inflammatory oral disease that affects approximately 42% of adults 30 years of age or older in the United States. In response to microbial dysbiosis within the periodontal pockets surrounding teeth, the host immune system generates an inflammatory environment in which soft tissue and alveolar bone destruction occur. The objective of this study was to identify diagnostic biomarkers and the mechanistic drivers of inflammation in periodontitis to identify drugs that may be repurposed to treat chronic inflammation. A meta-analysis comprised of two independent RNA-seq datasets was performed. RNA-seq analysis, signal pathway impact analysis, protein-protein interaction analysis, and drug target analysis were performed to identify the critical pathways and key players that initiate inflammation in periodontitis as well as to predict potential drug targets. Seventy-eight differentially expressed genes, 10 significantly impacted signaling pathways, and 10 hub proteins in periodontal gingival tissue were identified. The top 10 drugs that may be repurposed for treating periodontitis were then predicted from the gene expression and pathway data. The efficacy of these drugs in treating periodontitis has yet to be investigated. However, this analysis indicates that these drugs may serve as potential therapeutics to treat inflammation in gingival tissue affected by periodontitis.

Comparative transcriptome analysis of periodontitis and peri‐implantitis in human subjects

“…Among the validated upregulated genes, CSF2RB was increased 100-fold in both periodontitis and peri-implantitis gingival tissues compared with the healthy state. CSF2RB, also known to increase expression in other periodontitis studies, is a gene that belongs to the "cytokine-cytokine receptor interaction" identified by KEGG pathway analysis, 34,35 which is known to participate in various signaling pathways, such as IL-3, IL-5, and granulocyte-macrophage colony-stimulating factor/CSF2 pathways. 36 Including CSF3 and MS4A1, which have previously been revealed as potential diagnostic markers for periodontitis, 34 many immune response genes, such as C3, CD79A, SELP, and IGHG2, were also confirmed to have increased expression in periodontitis and peri-implantitis, suggesting an active immune response during disease progression.…”

Secondary Analysis of Human Bulk RNA-seq Dataset Suggest Potential Mechanisms and Transcriptional Biomarkers for Letrozole Resistance in Estrogen Positive (ER+) Breast Cancer

“…Separately, the Salmon read counts for each sample were combined into a tabular format and labeled as "resistant" or "sensitive" to treatment with Letrozole. This table was then used as input to the XGboost algorithm [39], which uses a tree-based method to train a model from 80% of the dataset and then quantifies its performance using the remaining 20% of the data [40,41], which minimizes model overfit. For the initial analysis, the gain metric was calculated from the read counts for all detected genes across all samples.…”

“…Given the gain metrics from the whole transcriptome, the number of genes/features being evaluated was reduced to the best two biomarkers from the original analysis since this is a number that is easily accommodated by qRT-PCR (or similar) molecular methods. This approach has been successfully applied previously with acceptable performance and accuracy [40,41,123]. The XGboost algorithm was selected since prior work has shown that tree-based classifiers are faster and more accurate than other machine learning-based methods such as support vector machine, neural networks, and Bayesian approaches [126].…”

Immunomodulatory role of oral microbiota in inflammatory diseases and allergic conditions

“…Thus, further investigation on how treatment of oral inflammation impacts other inflammatory diseases can provide novel approaches to treating conditions such as Crohn's disease, cardiovascular disease, and Alzheimer's disease (Figure 2). Recently, our group performed a meta-analysis on publicly available RNAseq data to identify inflammatory mediators of periodontitis that contribute to local and systemic inflammation and potential drugs that can be repurposed to reduce inflammation (82). By performing analysis of differentially expressed genes and pathways, we determined that FDA-approved drugs that target IL-6 receptor, RANKL, and IFNα/ β receptor were among our top candidates to reduce chronic inflammation during periodontitis.…”