Prediction of antipsychotics gene targets by integration of genomic, evolutionary, and gene expression data

Abstract

Background and aims:

Control of gene expression is essential for the establishment and maintenance of all cell types, and is involved in pathogenesis of several diseases. Accurate computational predictions of transcription factor regulation may thus help in understanding complex diseases, including mental disorders in which dysregulation of neural gene expression is thought to play a key role. However, predictions via bioinformatics tools are typically poorly specific.

Methods:

We have developed and tested a computational workflow to computationally predict Transcription Factor Binding Sites on proximal promoters of vertebrate genes. The computational framework was applied to groups of genes found to respond to antipsychotic drugs. Our approach for the prediction of regulatory elements is based on a search for known regulatory motifs retrieved from TRANSFAC, on DNA sequences of genes' promoters. Predictions are thus weighted by conservation. These predictions are further refined using a logistic regression to integrate data from co-regulated genes.

Results:

Consistent results were obtained on a large simulated dataset consisting of 5460 simulated promoter sequences, and on a set of 377 vertebrate gene promoters for which binding sites are known (TRANSFAC gene set).

Conclusions:

Our results show that integrating information from multiple data sources, such as genomic sequence of genes' promoters, conservation over multiple species, and gene expression data, can improve the accuracy of computational predictions. The results of predictions on genes involved in antipsychotics response include the drug target Homer 1, involved in glutamate synaptic plasticity response.