4.7 Article

Gene coexpression network during ontogeny in the yellow fever mosquito, Aedes aegypti

Journal

BMC GENOMICS
Volume 24, Issue 1, Pages -

Publisher

BMC
DOI: 10.1186/s12864-023-09403-4

Keywords

Mosquito; Ontogeny; Aedes aegypti; Gene coexpression network; WGCNA

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In this study, a gene coexpression network was constructed to explore the ontogeny of Ae. aegypti. Six gene modules and their intramodular hub genes associated with various developmental processes were identified. These findings provide valuable insights into potential molecular targets for disease control.
BackgroundThe behaviors and ontogeny of Aedes aegypti are closely related to the spread of diseases caused by dengue (DENV), chikungunya (CHIKV), Zika (ZIKV), and yellow fever (YFV) viruses. During the life cycle, Ae. aegypti undergoes drastic morphological, metabolic, and functional changes triggered by gene regulation and other molecular mechanisms. Some essential regulatory factors that regulate insect ontogeny have been revealed in other species, but their roles are still poorly investigated in the mosquito.ResultsOur study identified 6 gene modules and their intramodular hub genes that were highly associated with the ontogeny of Ae. aegypti in the constructed network. Those modules were found to be enriched in functional roles related to cuticle development, ATP generation, digestion, immunity, pupation control, lectins, and spermatogenesis. Additionally, digestion-related pathways were activated in the larvae and adult females but suppressed in the pupae. The integrated protein-protein network also identified cilium-related genes. In addition, we verified that the 6 intramodular hub genes encoding proteins such as EcKinase regulating larval molt were only expressed in the larval stage. Quantitative RT-PCR of the intramodular hub genes gave similar results as the RNA-Seq expression profile, and most hub genes were ontogeny-specifically expressed.ConclusionsThe constructed gene coexpression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. Ultimately, these findings will be key in identifying potential molecular targets for disease control.

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