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Updated: Feb 14, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
Published on: May 9, 2017
De novo transcriptome assembly and analysis of differential gene expression following peptidoglycan (PGN) challenge
Yu Liu1, Zhao-Zhe Xin1, Dai-Zhen Zhang2
1Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Synthetic Innovation Center for Coastal Bio-agriculture, Jiangsu Provincial Key Laboratory of Coastal Wetland Bioresources and Environmental Protection, School of Ocean and Biological Engineering, Yancheng Teachers University, Yancheng 224007, Jiangsu Province, People's Republic of China; College of Biotechnology and Pharmaceutical Engineering, Nanjing University of Technology, Nanjing 210009, People's Republic of China.
Abstract:
Antheraea pernyi is not only an important economic insect, it is increasingly employed as a model organism due to a variety of advantages, including ease of rearing and experimental manipulation compared with other Lepidoptera. Peptidoglycan (PGN) is a major component of the bacterial cell wall, and interactions between PGN and A. pernyi cause a series of physiological changes in the insect. In the present study, we constructed cDNA libraries from a A. pernyi PGN-infected group and a control group stimulated with phosphate-buffered saline (PBS). The transcriptome was de novo assembled using the Trinity platform, and 1698 differentially expressed genes (DEGs) were identified, comprising 894 up-regulated and 804 down-regulated genes. To further investigate immune-related DEGs, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were performed. GO analysis identified major immune-related GO terms and KEGG enrichment indicated gene responses to three pathways related to the insect immune system. Several homologous genes related to the immune response of the A. pernyi fat body post-PGN infection were identified and categorised. Taken together, the results provide insight into the complex molecular mechanisms of the responses to bacterial infection at the transcriptional level.
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