Prioritization of candidate genes for periodontitis using multiple computational tools.
BACKGROUND
Both genetic and environmental factors contribute to the development of periodontitis. Genetic studies identified a variety of candidate genes for periodontitis. The aim of the present study is to identify the most promising candidate genes for periodontitis using an integrative gene ranking method.


METHODS
Seed genes that were confirmed to be associated with periodontitis were identified using text mining. Three types of candidate genes were then extracted from different resources (expression profiles, genome-wide association studies). Combining the seed genes, four freely available bioinformatics tools (ToppGene, DIR, Endeavour, and GPEC) were integrated for prioritization of candidate genes. Candidate genes that identified with at least three programs and ranked in the top 20 by each program were considered the most promising.


RESULTS
Prioritization analysis resulted in 21 promising genes involved or potentially involved in periodontitis. Among them, IL18 (interleukin 18), CD44 (CD44 molecule), CXCL1 (chemokine [CXC motif] ligand 1), IL6ST (interleukin 6 signal transducer), MMP3 (matrix metallopeptidase 3), MMP7, CCR1 (chemokine [C-C motif] receptor 1), MMP13, and TLR9 (Toll-like receptor 9) had been associated with periodontitis. However, the roles of other genes, such as CSF3 (colony stimulating factor 3 receptor), CD40, TNFSF14 (tumor necrosis factor receptor superfamily, member 14), IFNB1 (interferon-β1), TIRAP (toll-interleukin 1 receptor domain containing adaptor protein), IL2RA (interleukin 2 receptor α), ETS1 (v-ets avian erythroblastosis virus E26 oncogene homolog 1), GADD45B (growth arrest and DNA-damage-inducible 45 β), BIRC3 (baculoviral IAP repeat containing 3), VAV1 (vav 1 guanine nucleotide exchange factor), COL5A1 (collagen, type V, α1), and C3 (complement component 3), have not been investigated thoroughly in the process of periodontitis. These genes are mainly involved in bacterial infection, immune response, and inflammatory reaction, suggesting that further characterizing their roles in periodontitis will be important.


CONCLUSIONS
A combination of computational tools will be useful in mining candidate genes for periodontitis. These theoretical results provide new clues for experimental biologists to plan targeted experiments.
Department of Periodontology and Oral Mucosa, Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Yuanbo
Y
Zhan
Ruimin
R
Zhang
Hongchao
H
Lv
Xuejing
X
Song
Xiaoman
X
Xu
Lin
L
Chai
Wenhua
W
Lv
Zhenwei
Z
Shang
Yongshuai
Y
Jiang
Ruijie
R
Zhang
D019295:Computational Biology; D016207:Cytokines / Q000235:genetics; D057225:Data Mining; D019992:Databases as Topic; D020869:Gene Expression Profiling; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D007378:Interleukins / Q000235:genetics; D020782:Matrix Metalloproteinases / Q000235:genetics; D010518:Periodontitis / Q000235:genetics*; D048069:Tumor Necrosis Factors / Q000235:genetics
D016207:Cytokines; D007378:Interleukins; D048069:Tumor Necrosis Factors; D020782:Matrix Metalloproteinases
Computational biology; genetic association studies; genomics; periodontitis.