Network-based study reveals potential infection pathways of hepatitis-C leading to various diseases.

Protein-protein interaction network-based study of viral pathogenesis
has been gaining popularity among computational biologists in recent
days. In the present study we attempt to investigate the possible
pathways of hepatitis-C virus (HCV) infection by integrating the
HCV-human interaction network, human protein interactome and human
genetic disease association network. We have proposed quasi-biclique and
quasi-clique mining algorithms to integrate these three networks to
identify infection gateway host proteins and possible pathways of HCV
pathogenesis leading to various diseases. Integrated study of three
networks, namely HCV-human interaction network, human protein
interaction network, and human proteins-disease association network
reveals potential pathways of infection by the HCV that lead to various
diseases including cancers. The gateway proteins have been found to be
biologically coherent and have high degrees in human interactome
compared to the other virus-targeted proteins. The analyses done in this
study provide possible targets for more effective anti-hepatitis-C
therapeutic involvement.

No comments: