A systems level based model for identifying potential taargets targets associated with influenza a infection

Abstract

Developing therapeutics for infectious diseases requires understanding the main processes driving host and pathogen through which molecular interactions influence cellular functions. The outcome of those infectious diseases, including influenza A (IAV) depends greatly on how the host responds to the virus and how the virus manipulates the host, which is facilitated by protein-protein functional inter-actions and analyzing infection associated genes at the systems level, which may enable us to characterize specific molecular mechanisms which allow the virus of influenza A strains H1N1 and H3N2 to persist and survive inside the host. The system level analysis based on experimental and computational approaches was used to predict human protein-protein functional inter-actions. This human proteinprotein functional interaction is a graph consisting of nodes which are proteins, and links joining them. Using this graph, we analyse topological properties of this human protein-protein functional interactions, identify candidate proteins using centrality measures and a map set of IAV infection associated proteins to elucidate genes related to IAV infection and identify essential dense sub-graphs underlying IAV infection outcome. We performed functional closeness and enrichment analyses to identify statistically and biologically significant processes and pathways implicated in IAV infection. These IAV infection associated proteins have shown to be relevant for further research towards new drugs and vaccine development. This study enhances our understanding on the interplay between influenza A and its host and may contribute to the process of designing novel drugs.