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

ProTINA takes advantage of the availability of comprehensive protein-protein and protein-DNA interaction databases to construct, when possible, a tissue or cell type-specific PGRN. The method considers a PGRN with weighted directed edges (see Figure 1A) , describing direct and indirect gene transcriptional regulation by TFs and their protein partners. The edge weights are determined by applying ridge regression using the gene expression data based on a kinetic model of the gene transcriptional process (see Figure 1B and Materials and Methods). Here, a positive weight indicates a gene activation, while a negative weight implies a gene repression. Because of the underlying kinetic model, ProTINA is able to incorporate dynamical gene expression data, a common type of data from drug treatment studies (5, (24) (25) (26) . The scoring of drug targets is based on the enhancement or attenuation of protein-gene regulatory interactions caused by the drug treatment. A drug-induced gene regulatory enhancement occurs when the expression of genes that are positively (negatively) regulated by a candidate target, becomes higher (lower) in drug treated samples than what is predicted by the PGRN model (see Figure 1C) . A drug-induced attenuation describes the opposite scenario, where the expression of positively (negatively) regulated genes of a target is lower (higher) than expected from the model. For any given differential gene expression sample, a candidate protein target is scored based on the overall enhancement and/or attenuation of its regulatory influence on the downstream genes (see Figure 1D and Materials and Methods). Thus, a protein target with a more positive (negative) score is considered a more likely target of the drug, in which the drug treatment enhances (attenuates) the gene regulatory activity.