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

In this work, we developed ProTINA (Protein Target Inference by Network Analysis), a network perturbation analysis method for protein target identification using gene transcriptional profiles. The analysis involves two key steps: (a) the creation of a model of tissue or cell type-specific protein-gene regulatory network (PGRN) and (b) the calculation of protein target scores based on the enhancement or attenuation of the protein-gene regulations. In developing ProTINA, we addressed some of the drawbacks in the existing methods. First, the PGRN in ProTINA is based on a dynamic model of the gene transcriptional process, and is therefore able to take advantage of time-series gene expression profiles that are commonly generated by drug treatment studies. In addition, ProTINA leverages on the availability of comprehensive maps of protein-protein and protein-DNA interactions for the construction of the PGRN, which serves as prior information to alleviate network inferability issue. Finally, ProTINA scores candidate targets based on drug-induced perturbations to the expression of genes regulated by the targets, rather than the expression of the targets themselves. We demonstrated the superiority of ProTINA over the state-of-the-art method De-MAND and differential gene expression analysis (DE), in predicting the protein targets of drugs using human and mouse datasets from NCI-DREAM drug synergy challenge (24) , genotoxicity study (25) and chromosome drug targeting study (26) . Besides protein targets of compounds, we presented the application of ProTINA to study hostpathogen interactions, specifically for elucidating the targets of influenza A viral proteins.