A Computational Network Biology Approach to Understand Salinity Stress Response in Rice (Oryza Sativa L.)

Abstract

Rice (Oryza sativa L.), the major staple food for more than half of world’s population, is being seriously affected by salinity stress worldwide. Salinity tolerance in rice is governed by many genes, identification of these stress responsive key genes as well as understanding the underlying cellular mechanisms is of paramount importance for developing salt tolerant varieties. In this study, meta-analysis was performed to combine gene expression gene expression datasets related to the identification of salinity stress responsive genes. A two-stage filtering approach was used to initially identify relevant genes. Then, a weighted gene co-expression network analysis was performed to detect the various gene modules associated with salinity stress in rice followed by DHGA approach to detect hub genes and unique hub genes. Moreover, other bioinformatics tools and techniques like Gene Ontology, motif analysis, protein structure prediction and protein-protein interactions were used to understand the salinity stress response mechanism in rice. Through the hub gene detection approach, 167 and 178 hub genes were identified in salinity stress and normal condition respectively, where 121 hub genes were common to both the conditions and 46 were unique to salinity stress condition. The functional enrichment analysis of hub genes further revealed their involvement in various processes linked with the salinity stress in rice. The 46 salinity stress genes were further analyzed with QTL, protein-protein interaction, gene ontology and motif analysis. These identified genes and mechanisms will add to the understanding of salinity response and its regulation in rice.