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Identification of key genes associated with overwintering in Anoplophora glabripennis larva using gene co-expression network analysis.

Authors :
Xu Y
Li Y
Wang Q
Zheng C
Zhao D
Shi F
Liu X
Tao J
Zong S
Source :
Pest management science [Pest Manag Sci] 2021 Feb; Vol. 77 (2), pp. 805-816. Date of Electronic Publication: 2020 Oct 09.
Publication Year :
2021

Abstract

Background: Anoplophora glabripennis (Coleoptera: Cerambycidae) is a major quarantine pest in forestry. It is widely distributed throughout many regions such as Asia, Europe, and North America, and has enormous destructive potential for forests. The larvae of A. glabripennis overwinter in a dormant state with strong cold tolerance, and whether the larvae survive winter determines the population density in the following year. However, the molecular mechanisms of this process are not clear.<br />Results: RNA sequencing (RNA-Seq) analysis of A. glabripennis larvae at five overwintering stages identified 6876 differentially expressed genes (DEGs). Among these, 46 functional genes that might respond to low temperature were identified. Weighted gene co-expression network analysis revealed that the MEturquoise module was correlated with the overwintering process. The STPK, PP2A, DGAT, and HSF genes were identified as hub genes using visualization of gene network. In addition, four genes related to sugar transport, gluconeogenesis and glycosylation were screened, which may be involved in the metabolic regulation of overwintering larvae. The protein-protein interaction network indicated that ribosomal protein and ATP synthase may play an important role in connecting with other proteins. The expression levels of fifteen hub genes were further validated by quantitative RT-PCR, and the results were consistent with RNA-Seq.<br />Conclusion: This study demonstrates key genes that may reveal the molecular mechanism of overwintering in A. glabripennis larvae. The genes may be the potential targets to prevent larvae from surviving the cold winter by developing new biological agents using genetic engineering.<br /> (© 2020 Society of Chemical Industry.)

Details

Language :
English
ISSN :
1526-4998
Volume :
77
Issue :
2
Database :
MEDLINE
Journal :
Pest management science
Publication Type :
Academic Journal
Accession number :
32909651
Full Text :
https://doi.org/10.1002/ps.6082