Analysis of protein-protein interaction network based on altered genes expressed in lung tissue for avian influenza disease

Abstract

The spread of bird flu disease with high mortality, reduced egg production and forced killing of infected flocks has a devastating effect on the poultry industry. Based on the central protein, the virus is divided into three subgroups A, B, and C. Influenza type A virus is very common. This virus can infect different species of mammals and birds, and it is more pathogenic than influenza B and C, and it causes epidemics of different severities almost every year. Breeding goals to maintain the health of animals lead to the well-being of animals and prevent economic losses. The purpose of this research was to use biological network analysis method to identify new key markers involved in bird flu disease. In this study, in order to quickly obtain the most important genes involved in this disease, gene expression microarray data related to the lung tissue of chickens infected with the virus was prepared from the GEO site with the accession number GSE53931. The protein-protein interaction network was drawn by Cytoscape software. Protein complexes were analyzed using MCODE. Three protein complexes were extracted. The seed proteins in the complexes include RPL10A, NOB1, and MSH4, which were introduced as key markers, as well as NEB, RPF1, CSRNP1, RNF146, and RPL18A genes with the lowest level of expression and SLC6A20, PLN, SLC17A8 ,NOXO1 and TMEM179 gene with the highest level of expression in the present study. Most of the genes are involved in the transcription, replication, translation of the gene in the influenza virus. Network-based analyzes can detect effective genes and functional modules, which can significantly help in identifying regulatory factors and biological processes involved in the response to avian influenza, and subsequently, better control of the disease.