Bioinformatic prediction of leader genes in human periodontitis.
BACKGROUND
Genes involved in different biologic processes form complex interaction networks. However, only a few have a high number of interactions with the other genes in the network. In previous bioinformatics and experimental studies concerning the T lymphocyte cell cycle, these genes were identified and termed "leader genes." In this work, genes involved in human periodontitis were tentatively identified and ranked according to their number of interactions to obtain a preliminary, broader view of molecular mechanisms of periodontitis and plan targeted experimentation.


METHODS
Genes were identified with interrelated queries of several databases. The interactions among these genes were mapped and given a significance score. The weighted number of links (weighted sum of scores for every interaction in which the given gene is involved) was calculated for each gene. Genes were clustered according to this parameter. The genes in the highest cluster were termed leader genes.


RESULTS
Sixty-one genes involved or potentially involved in periodontitis were identified. Only five were identified as leader genes, whereas 12 others were ranked in an immediately lower cluster. For 10 of 17 genes there is evidence of involvement in periodontitis; seven new genes that are potentially involved in this disease were identified. The involvement in periodontitis has been completely established for only two leader genes.


CONCLUSIONS
We applied a validated bioinformatics algorithm to increase our knowledge of molecular mechanisms of periodontitis. Even with the limitations of this ab initio analysis, this theoretical study can suggest ad hoc experimentation targeted on significant genes and, therefore, simpler than mass-scale molecular genomics. Moreover, the identification of leader genes might suggest new potential risk factors and therapeutic targets.
School of Dental Medicine, University of Genoa, Genoa, Italy.
Ugo
U
Covani
Simone
S
Marconcini
Luca
L
Giacomelli
Victor
V
Sivozhelevov
Antonio
A
Barone
Claudio
C
Nicolini
D002453:Cell Cycle / Q000235:genetics; D002874:Chromosome Mapping; D019295:Computational Biology; D030541:Databases, Genetic; D005544:Forecasting; D051380:GRB2 Adaptor Protein / Q000235:genetics; D005796:Genes / Q000235:genetics; D023281:Genomics; D006801:Humans; D008957:Models, Genetic; D008967:Molecular Biology; D052002:NF-kappa B p50 Subunit / Q000235:genetics; D010518:Periodontitis / Q000235:genetics*; D019869:Phosphatidylinositol 3-Kinases / Q000235:genetics; D050721:Proto-Oncogene Proteins c-cbl / Q000235:genetics; D012680:Sensitivity and Specificity; D013601:T-Lymphocytes / Q000502:physiology; D051996:Transcription Factor RelA / Q000235:genetics
D051380:GRB2 Adaptor Protein; C495948:GRB2 protein, human; D052002:NF-kappa B p50 Subunit; C489548:NFKB1 protein, human; C498143:RELA protein, human; D051996:Transcription Factor RelA; D050721:Proto-Oncogene Proteins c-cbl; D019869:Phosphatidylinositol 3-Kinases; C067419:CBL protein, human