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

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.

Comparison of Human Selenoprotein P Determinants in Serum between Our Original Methods and Commercially Available Kits.

Selenoprotein P (SeP) is a selenium (Se)-rich extracellular protein. SeP is identified as a hepatokine, causing insulin resistance in type 2 diabetes. Thus, the measurement of SeP in serum has received much attention, and several enzyme-linked immunosorbent assay (ELISA) kits for SeP determination are now commercially available. In the present study, we determined the serum SeP levels by our original ELISA and sol particle homogeneous immunoassay (SPIA) methods and also by commercially available kits, and these determinants were compared. We found a kit-dependent correlation of the determinants with our methods. These results suggest that the selection of kit is critical for comparison with our previous reports and for discussing the relationship between the serum SeP levels and disease condition.

Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis.

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

Gene expression profiling of replicative and induced senescence.

Cellular senescence is a cell cycle arrest accompanied by high expression of cyclin dependent kinase inhibitors which counteract overactive growth signals, which serves as a tumor suppressive mechanism. Senescence can be a result of telomere shortening (natural or replicative senescence) or DNA damage resulting from exogenous stressors (induced senescence). Here, we performed gene expression profiling through RNA-seq of replicative senescence, adriamycin-induced senescence, H2O2-induced senescence, and 5-aza-2-deoxycytidine-induced senescence in order to profile the pathways controlling various types of senescence. Overall, the pathways common to all 4 types of senescence were related to inflammation and the innate immune system. It was also evident that 5-aza-induced senescence mirrors natural replicative senescence due to telomere shortening. We also examined the prevalence of senescence-associated secretory phenotype (SASP) factors in the RNA-seq data, showing that it is a common characteristic of all 4 types of senescence. In addition, we could discriminate changes in gene expression due to quiescence during cellular senescence from those that were specific to senescence.