Abstract: IFNγ, a cytokine promoting cell-mediated immunity and
antiviral effects, regulates the expression of a large set of genes involved in
the immune response. Based on logistic regression, an in silico model for
predicting IFNγ regulated transcription has been developed by scoring the
transcription factor binding sites on the putative promoters of regulated
versus not regulated genes derived from the microarray data of IFNγ
treated human macrophages. The model effectively discriminates the
transcription factor binding sites that confer responsiveness to IFNγ
from those that do not. The model has 65% true positive and 22% false positive
rates when evaluated on a small validation set. In order to identify potential
IFNγ regulated genes in the whole genome, the model has been used to
screen 13,668 promoter pairs of human-mouse orthologs/homologs from Ensembl,
and 1,387 of them were predicted to be potentially regulated by IFNγ. In
the pilot experiment, the regulation pattern of a subset of predicted genes
that were not detected by microarray approach was evaluated by quantitative
PCR. The results for the four novel genes, which are up regulated by IFNγ
in human macrophages and identified by this approach, are described in the
present communication.
Keywords: IFNγ, transcription, transcription factor binding site (TFBS), promoter, logistic regression model
