Identification of Synchronized Role of Transcription Factors, Genes, and Enzymes in Arabidopsis thaliana under Four Abiotic Stress Responsive Pathways

Microarray datasets are widely used resources to predict and characterize functional entities of the whole genomics. The study initiated here aims to identify overexpressed stress responsive genes using microarray datasets applying in silico approaches. The target also extended to build a protein-protein interaction model of regulatory genes with their upstream and downstream connection in Arabidopsis thaliana. Four microarray datasets generated treating abiotic stresses like salinity, cold, drought, and abscisic acid (ABA) were chosen. Retrieved datasets were firstly filtered based on their expression comparing to control. Filtered datasets were then used to create an expression hub. Extensive literature mining helped to identify the regulatory molecules from the expression hub. The study brought out 42 genes/TF/enzymes as the role player during abiotic stress response. Further bioinformatics study and also literature mining revealed that thirty genes from those forty-two were highly correlated in all four datasets and only eight from those thirty genes were determined as highly responsive to the above abiotic stresses. Later their protein-protein interaction (PPI), conserved sequences, protein domains, and GO biasness were studied. Some web based tools and software like String database, Gene Ontology, InterProScan, NCBI BLASTn suite, etc. helped to extend the study arena.