Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in Metisa plana Hormone Pathways

SIMPLE SUMMARY: The increasingly growing demand for palm oil (PO) blankets heavy pressure for sustainable PO production. PO, the world’s most efficient oilseed crop, is being used in various applications, including food, cosmetics, and biofuels. However oil palm is always at risk being exposed to Metisa plana infestation, which contributes to the negative impacts on the oil palm plantations and industries. Therefore, conventional pesticides are routinely applied as a control measure in outbreak management. Nevertheless, the perpetual occurrences of M. plana infestation remain, affecting the overall yield productivity. This study uses an integrated bioinformatics approach, including a gene co-expression network and clustering method, to screen, identify, and determine the key genes involved in controlling insect hormone biosynthesis with ecdysone and juvenile hormones among them. The findings provide key regulatory genes (Hnf4, Hr4, MED14, Usp, Tai, and Trr) that could be used as potential targets for gene silencing technologies such as RNA interference (RNAi) desired in understanding M. plana gene function. ABSTRACT: Metisa plana Walker (Lepidoptera: Psychidae) is a major oil palm pest species distributed across Southeast Asia. M. plana outbreaks are regarded as serious ongoing threats to the oil palm industry due to their ability to significantly reduce fruit yield and subsequent productivity. Currently, conventional pesticide overuses may harm non-target organisms and severely pollute the environment. This study aims to identify key regulatory genes involved in hormone pathways during the third instar larvae stage of M. plana gene co-expression network analysis. A weighted gene co-expression network analysis (WGCNA) was conducted on the M. plana transcriptomes to construct a gene co-expression network. The transcriptome datasets were obtained from different development stages of M. plana, i.e., egg, third instar larvae, pupa, and adult. The network was clustered using the DPClusO algorithm and validated using Fisher’s exact test and receiver operating characteristic (ROC) analysis. The clustering analysis was performed on the network and 20 potential regulatory genes (such as MTA1-like, Nub, Grn, and Usp) were identified from ten top-most significant clusters. Pathway enrichment analysis was performed to identify hormone signalling pathways and these pathways were identified, i.e., hormone-mediated signalling, steroid hormone-mediated signalling, and intracellular steroid hormone receptor signalling as well as six regulatory genes Hnf4, Hr4, MED14, Usp, Tai, and Trr. These key regulatory genes have a potential as important targets in future upstream applications and validation studies in the development of biorational pesticides against M. plana and the RNA interference (RNAi) gene silencing method.