Mining of Stress-related Genes in Pigmented and Non-pigmented Rice using Gene Co-expression Network and Clustering Approaches
DOI:
https://doi.org/10.11113/mjfas.v18n5.2543Keywords:
Clustering gene co-expression network, MCL, pigmented rice, stress-resistantAbstract
Pigmented rice has been associated with stress-resistant traits, such as resistance to abiotic and biotic stresses, but the genes network that is responsible for such traits remains limited. Hence, this study aims to identify stress-related genes in the pigmented rice using computational approaches. The gene co-expression network was constructed using Pearson Correlation Coefficient (PCC) ≥ 0.9 among differentially expressed genes (DEGs) of trancriptomes between pigmented and non-pigmented rice. The gene co-expression network was clustered using Markov Cluster algorithm (MCL) to identify the functional modules and the hub genes for each module were determined. The functional analyses were performed to each module to determine the related gene ontology (GO) and pathway. Protein-protein interaction (PPI) from STRING database was used to validate the functional analyses. A total of 721 DEGs were used to construct the gene co-expression network of pigmented and non-pigmented rice varieties. Of these, 614 DEGs with 15,259 edges were identified by PCC. Using MCL, 10 clusters were identified in the gene co-expression network of pigmented and non-pigmented rice varieties. Three clusters were enriched with seven GO terms (i.e., response to stress, response to stimulus, transcription factor activity) that are related to stress-resistant traits, indicating the highly correlated genes were the stress-related genes. Interestingly, nine hub genes were found to be related to drought tolerance, disease resistance and hormone biosynthesis. Validation of hub genes using STRING database revealed that 48 hub genes were also connected in the PPI network, suggesting their potential as candidate proteins in stress-related traits. This study demonstrated that the molecular interaction network and the network clustering approach are efficient in identifying the stress-related genes, which could provide new insights in understanding plant responses to abiotic and biotic stresses.
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