Abstract
Abiotic and biotic stresses adversely affect plant growth and development and eventually result in less yield and threaten food security worldwide. In plants, several studies have been carried out to understand molecular responses to abiotic and biotic stresses. However, the complete circuitry of stress-responsive genes that plants utilise in response to those environmental stresses are still unknown. The protein phosphatase 2A (PP2A) gene has been known to have a crucial role in abiotic and biotic stresses; but how it regulates the stress response in plants is still not known completely. In this study, we constructed gene co-expression networks of PP2A genes with stress-responsive gene datasets from cold, drought, heat, osmotic, genotoxic, salt, and wounding stresses to unveil their relationships with the PP2A under different conditions of stress. The graph analysis identified 13 hub genes and several influential genes based on closeness centrality score (CCS). Our findings also revealed the count of unique genes present in different settings of stresses and subunits. We also formed clusters of influential genes based on the stress, CCS, and co-expression value. Analysis of cis-regulatory elements (CREs), recurring in promoters of these genes was also performed. Our study has led to the identification of 16 conserved CREs.
Highlights
Gene co-expression network analysis (GCNA) has attracted a lot of interest recently in plants, and various methods have been introduced for their inference biologically and statistically from a large amount of gene expression data
Since phosphatase 2A (PP2A) is a diverse enzyme in nature it is known as an important regulator in a wide range of plant processes such as ROS signaling, light acclimation, biotic and abiotic stress like heat, salinity, d rought[9]
We look at the properties of these genes and find that five genes identified in cluster 2 are present (1) only once in the dataset, (2) present in drought stress condition, (3) have same closeness centrality score (CCS) of 0.331508, and (4) have a negligible difference in co-expression values
Summary
Gene co-expression network analysis (GCNA) has attracted a lot of interest recently in plants, and various methods have been introduced for their inference biologically and statistically from a large amount of gene expression data. Since PP2A is a diverse enzyme in nature it is known as an important regulator in a wide range of plant processes such as ROS signaling, light acclimation, biotic and abiotic stress like heat, salinity, d rought[9]. Building and analysis of co-expression networks are crucial for inferring gene functions, their annotations, biological pathways in which the group of genes is involved, candidate disease gene prioritisation and the identification of regulatory genes during stress conditions by examining a huge collection of complex d ata[10]. Microarray analysis is one of the crucial techniques to measure the expression of different genes simultaneously in a plant cell and provide functional data for those genes. Not many studies have investigated co-expression networks of PP2A in plants during stress conditions using microarray data. It is a prerequisite to select corresponding genes related to stress conditions from thousands of genes with the help of appropriate in-silico approaches or other computing measures such as distribution pattern or graph analysis
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