Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection

Noh, Heeju; Shoemaker, Jason E; Gunawan, Rudiyanto

Nucleic Acids Research, 2018, Vol. 46, No. 6 e34 Like ProTINA, the state-of-the-art method DeMAND also relies on gene transcriptional dysregulations to score drug targets. But, DeMAND does not consider the mode nor the dynamics of the gene regulations, and is unable to predict the direction of the drug-induced dysregulations. DeMAND calculates protein dysregulation scores (P-values) for a given gene regulatory network, by statistical comparison between samples from drug treatment and from control experiments. Consequently, DeMAND requires only few samples to generate its prediction (provided that the network can be defined a priori). On the other hand, ProTINA makes use of the available differential gene expression profiles from a study or a cell line (i.e. not only from the specific drug treatment), to assign the edge weights of the PGRN by ridge regression. Importantly, in the regression analysis, the PGRN model used in ProTINA accounts for the network perturbations. The ability of ProTINA to incorporate data from other drug treatments or conditions in the scoring of protein targets makes this method particularly suited to take advantage of the extensive and still growing number of gene transcriptional profiles from online databases, such as GEO. As demonstrated in the applications to three benchmark drug treatment datasets using human and mouse cell lines, ProTINA significantly outperformed DeMAND and the standard DE analysis. The target predictions of ProTINA also provide indications for the MoA of compounds, including the directions of the network perturbations, with high sensitivity and specificity.